research on ethical standards pdf

Ethical Principles in Research

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• D. J. Roy ,
• P. McL. Black ,
• B. McPeek &
• M. F. McKneally  

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Research ethics is as integral a part of scientific judgment as clinical ethics is of clinical judgment. 2 Many ethical issues in research arise from a failure to think as rigorously about the conditions for ethical consistency as about those for scientific validity. The ethical principles governing all surgical, clinical, and biomedical research with human subjects are fundamentally the same. They have been listed and discussed in numerous documents and countless publications over the past 40 years. 3-10 .

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Department of Surgery, University of Cologne, Ostmerheimer Strasse 200, D-51109, Cologne, Germany

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Roy, D.J., Black, P.M., McPeek, B., McKneally, M.F. (1998). Ethical Principles in Research. In: Troidl, H., McKneally, M.F., Mulder, D.S., Wechsler, A.S., McPeek, B., Spitzer, W.O. (eds) Surgical Research. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1888-3_60

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Ensuring ethical standards and procedures for research with human beings

Research ethics govern the standards of conduct for scientific researchers. It is important to adhere to ethical principles in order to protect the dignity, rights and welfare of research participants. As such, all research involving human beings should be reviewed by an ethics committee to ensure that the appropriate ethical standards are being upheld. Discussion of the ethical principles of beneficence, justice and autonomy are central to ethical review.

WHO works with Member States and partners to promote ethical standards and appropriate systems of review for any course of research involving human subjects. Within WHO, the Research Ethics Review Committee (ERC) ensures that WHO only supports research of the highest ethical standards. The ERC reviews all research projects involving human participants supported either financially or technically by WHO. The ERC is guided in its work by the World Medical Association Declaration of Helsinki (1964), last updated in 2013, as well as the International Ethical Guidelines for Biomedical Research Involving Human Subjects (CIOMS 2016).

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UNICEF’s strategic agenda to harness the power of evidence to drive change for children makes ethical conduct in evidence generation an organizational imperative. Ethical reflection and conduct as part of everyday evidence generation is necessary if we are to engage appropriately with all our stakeholders, including with the hardest to reach, the furthest from help and the most excluded.

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Ethics in scientific research: a lens into its importance, history, and future

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Introduction

Ethics are a guiding principle that shapes the conduct of researchers. It influences both the process of discovery and the implications and applications of scientific findings 1 . Ethical considerations in research include, but are not limited to, the management of data, the responsible use of resources, respect for human rights, the treatment of human and animal subjects, social responsibility, honesty, integrity, and the dissemination of research findings 1 . At its core, ethics in scientific research aims to ensure that the pursuit of knowledge does not come at the expense of societal or individual well-being. It fosters an environment where scientific inquiry can thrive responsibly 1 .

The need to understand and uphold ethics in scientific research is pertinent in today’s scientific community. First, the rapid advancement of technology and science raises ethical questions in fields like biotechnology, biomedical science, genetics, and artificial intelligence. These advancements raise questions about privacy, consent, and the potential long-term impacts on society and its environment 2 . Furthermore, the rise in public perception and scrutiny of scientific practices, fueled by a more informed and connected populace, demands greater transparency and ethical accountability from researchers and institutions.

This commentary seeks to bring to light the need and benefits associated with ethical adherence. The central theme of this paper highlights how upholding ethics in scientific research is a cornerstone for progress. It buttresses the fact that ethics in scientific research is vital for maintaining the trust of the public, ensuring the safety of participants, and legitimizing scientific findings.

Historical perspective

Ethics in research is significantly shaped by past experiences where a lack of ethical consideration led to negative consequences. One of the most striking examples of ethical misconduct is the Tuskegee Syphilis Study 3 conducted between 1932 and 1972 by the U.S. Public Health Service. In this study, African American men in Alabama were used as subjects to study the natural progression of untreated syphilis. They were not informed of their condition and were denied effective treatment, even after penicillin became available as a cure in the 1940s 3 .

From an ethical lens today, this is a gross violation of informed consent and an exploitation of a vulnerable population. The public outcry following the revelation of the study’s details led to the establishment of the National Commission for the Protection of Human Subjects of Biomedical and Behavioural Research 4 . This commission eventually produced the Belmont Report in 1979 4 , setting forth principles such as respect for persons, beneficence, and justice, which now underpin ethical research practices 4 .

Another example that significantly impacted ethical regulations was the thalidomide tragedy of the late 1950s and early 1960s 5 . Thalidomide was marketed as a safe sedative for pregnant women to combat morning sickness in Europe. Thalidomide resulted in the birth of approximately ten thousand children with severe deformities due to its teratogenic effects 5 , which were not sufficiently researched prior to the drug’s release. This incident underscored the critical need for comprehensive clinical testing and highlighted the ethical imperative of understanding and communicating potential risks, particularly for vulnerable groups such as pregnant women. In response, drug testing regulations became more rigorous, and the importance of informed consent, especially in clinical trials, was emphasized.

The Stanford Prison Experiment of 1971, led by psychologist Philip Zimbardo is another prime example of ethical oversight leading to harmful consequences 6 . The experiment, which aimed to study the psychological effects of perceived power, resulted in emotional trauma for participants. Underestimating potential psychological harm with no adequate systems to safeguard human participants from harm was a breach of ethics in psychological studies 6 . This case highlighted the necessity for ethical guidelines that prioritize the mental and emotional welfare of participants, especially in psychological research. It led to stricter review processes and the establishment of guidelines to prevent psychological harm in research studies. It influenced the American Psychological Association and other bodies to refine their ethical guidelines, ensuring the protection of participants’ mental and emotional well-being.

Impact on current ethical standards

These historical, ethical oversights have been instrumental in shaping the current landscape of ethical standards in scientific research. The Tuskegee Syphilis Study led to the Belmont Report in 1979, which laid out key ethical principles such as respect for persons, beneficence, and justice. It also prompted the establishment of Institutional Review Boards (IRBs) to oversee research involving human subjects. The thalidomide tragedy catalyzed stricter drug testing regulations and informed consent requirements for clinical trials. The Stanford Prison Experiment influenced the American Psychological Association to refine its ethical guidelines, placing greater emphasis on the welfare and rights of participants.

These historical episodes of ethical oversights have been pivotal in forging the comprehensive ethical frameworks that govern scientific research today. They serve as stark reminders of the potential consequences of ethical neglect and the perpetual need to prioritize the welfare and rights of participants in any research endeavor.

One may ponder on the reason behind the Tuskegee Syphilis Study, where African American men with syphilis were deliberately left untreated. What led scientists to prioritize research outcomes over human well-being? At the time, racial prejudices, lack of understanding of ethical principles in human research, and regulatory oversight made such studies pass. Similarly, the administration of thalidomide to pregnant women initially intended as an antiemetic to alleviate morning sickness, resulted in unforeseen and catastrophic birth defects. This tragedy highlights a critical lapse in the pre-marketing evaluation of drugs’ safety.

Furthermore, the Stanford prison experiment, designed to study the psychological effects of perceived power, spiraled into an ethical nightmare as participants suffered emotional trauma. This begs the question on how these researchers initially justified their methods. From today’s lens of ethics, the studies conducted were a complete breach of misconduct, and I wonder if there were any standards that guided primitive research in science.

Current ethical standards and guidelines in research

Informed consent.

This mandates that participants are fully informed about the nature of the research, including its objectives, procedures, potential risks, and benefits 7 , 8 . They must be given the opportunity to ask questions and must voluntarily agree to participate without coercion 7 , 8 . This ensures respect for individual autonomy and decision-making.

Confidentiality and privacy

Confidentiality is pivotal in research involving human subjects. Participants’ personal information must be protected from unauthorized access or disclosure 7 , 8 . Researchers are obliged to take measures to preserve the anonymity and privacy of participants, which fosters trust and encourages participation in research 7 , 8 .

Non-maleficence and beneficence

These principles revolve around the obligation to avoid harm (non-maleficence) and to maximize possible benefits while minimizing potential harm (beneficence) 7 , 8 . Researchers must ensure that their studies do not pose undue risks to participants and that any potential risks are outweighed by the benefits.

Justice in research ethics refers to the fair selection and treatment of research participants 8 . It ensures that the benefits and burdens of research are distributed equitably among different groups in society, preventing the exploitation of vulnerable populations 8 .

The role of Institutional Review Boards (IRB)

Institutional Review Boards play critical roles in upholding ethical standards in research. An IRB is a committee established by an institution conducting research to review, approve, and monitor research involving human subjects 7 , 8 . Their primary role is to ensure that the rights and welfare of participants are protected.

Review and approval

Before a study commences, the IRB reviews the research proposal to ensure it adheres to ethical guidelines. This includes evaluating the risks and benefits, the process of obtaining informed consent, and measures for maintaining confidentiality 7 , 8 .

Monitoring and compliance

IRB also monitors ongoing research projects to ensure compliance with ethical standards. They may require periodic reports and can conduct audits to ensure ongoing adherence to ethical principles 7 , 8 .

Handling ethical violations

In cases where ethical standards are breached, IRB has the authority to impose sanctions, which can range from requiring modifications to the study to completely halting the research project 7 , 8 .

Other agencies and boards enforcing standards

Beyond IRB, there are other regulatory bodies and agencies at national and international levels that enforce ethical standards in research. These include:

The Office for Human Research Protections (OHRP) in the United States, which oversees compliance with the Federal Policy for the Protection of Human Subjects.

The World Health Organization (WHO) , which provides international ethical guidelines for biomedical research.

The International Committee of Medical Journal Editors (ICMJE) , which sets ethical standards for the publication of biomedical research.

These organizations, along with IRB, form a comprehensive network that ensures the ethical conduct of scientific research. They safeguard the integrity of research using the reflections and lesson learnt from the past.

Benefits of ethical research

Credible and reliable outcomes, why is credibility so crucial in research, and how do ethical practices contribute to it.

Ethical practices such as rigorous peer review, transparent methodology, and adherence to established protocols ensure that research findings are reliable and valid 9 . When studies are conducted ethically, they are less likely to be marred by biases, fabrications, or errors that could compromise credibility. For instance, ethical standards demand accurate data reporting and full disclosure of any potential conflicts of interest 9 , which directly contribute to the integrity and trustworthiness of research findings.

How do ethical practices lead to socially beneficial outcomes?

Ethical research practices often align with broader societal values and needs, leading to outcomes that are not only scientifically significant but also socially beneficial. By respecting principles like justice and beneficence, researchers ensure that their work with human subjects contributes positively to society 7 , 8 . For example, ethical guidelines in medical research emphasize the need to balance scientific advancement with patient welfare, ensuring that new treatments are both effective and safe. This balance is crucial in addressing pressing societal health concerns while safeguarding individual rights and well-being.

Trust between the public and the scientific community

The relationship between the public and the scientific community is heavily reliant on trust, which is fostered through consistent ethical conduct in research. When the public perceives that researchers are committed to ethical standards, it reinforces their confidence in the scientific process and its outcomes. Ethical research practices demonstrate a respect for societal norms and values, reinforcing the perception that science serves the public good.

Case studies

Case study 1: the development and approval of covid-19 vaccines.

The development and approval of COVID-19 vaccines within a short time is a testament to how adherence to ethical research practices can achieve credible and beneficial outcomes. Strict adherence to ethical guidelines, even in the face of a global emergency, ensured that the vaccines were developed swiftly. However, safety standards were compromised to some extent as no animal trials were done before humans. The vaccine development was not transparent to the public, and this fuelled the anti-vaccination crowd in some regions. Ethical compliance, including rigorous testing and transparent reporting, should expedite scientific innovation while maintaining public trust.

Case study 2: The CRISPR babies

What ethical concerns were raised by the creation of the crispr babies, and what were the consequences.

The creation of the first genetically edited babies using CRISPR technology in China raised significant ethical concerns 10 . The lack of transparency, inadequate consent process, and potential risks to the children can be likened to ethical misconduct in genetic engineering research. This case resulted in widespread condemnation from the scientific community and the public, as well as international regulatory frameworks and guidelines for genetic editing research 10 .

Recommendation and conclusion

Continuous education and training.

The scientific community should prioritize ongoing education and training in ethics for researchers at all levels, ensuring awareness and understanding of ethical standards and their importance.

Enhanced dialogue and collaboration

Encourage multidisciplinary collaborations and dialogues between scientists, ethicists, policymakers, and the public to address emerging ethical challenges and develop adaptive guidelines.

Fostering a culture of ethical responsibility

Institutions and researchers should cultivate an environment where ethical considerations are integral to the research process, encouraging transparency, accountability, and social responsibility.

Global standards and cooperation

Work toward establishing and harmonizing international ethical standards and regulatory frameworks, particularly in areas like genetic engineering and AI, where the implications of research are global.

Ethics approval

Ethics approval was not required for this editorial.

Informed consent was not required for this editorial

Sources of funding

No funding was received for this research.

Author contribution

G.D.M. wrote this paper.

Conflicts of interest disclosure

The authors declare no conflicts of interest.

Research registration unique identifying number (UIN)

Goshen David Miteu.

Data availability statement

Provenance and peer review.

Not commissioned, externally peer-reviewed.

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Published online 21 March 2024

SRCD Governing Council \(2007, March\). Ethical standards in research. Society for Research in Child Development . Retrieved from https://www.srcd.org/about-us/ethical-standards-research

CITE AS:

SRCD Gov\ erning Council \(2007, March\). Ethical standards in research. Society for Research in Child Development . https://www.srcd.org/about-us/ethical-standards-research

SRCD Governing Council \(2007\). Ethical s\ tandards in research. Society for Research in Child Development . https://www.srcd.org/about-us/ethical-standards-research

SRCD Governing Council \(2007, March\). Ethical standards in research. Society for Research in Child Development. https://www.srcd.org/about-us/ethical-standards-research

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Methodology

• Ethical Considerations in Research | Types & Examples

Ethical Considerations in Research | Types & Examples

Published on October 18, 2021 by Pritha Bhandari . Revised on May 9, 2024.

Ethical considerations in research are a set of principles that guide your research designs and practices. Scientists and researchers must always adhere to a certain code of conduct when collecting data from people.

The goals of human research often include understanding real-life phenomena, studying effective treatments, investigating behaviors, and improving lives in other ways. What you decide to research and how you conduct that research involve key ethical considerations.

These considerations work to

• protect the rights of research participants
• enhance research validity
• maintain scientific or academic integrity

Table of contents

Why do research ethics matter, getting ethical approval for your study, types of ethical issues, voluntary participation, informed consent, confidentiality, potential for harm, results communication, examples of ethical failures, other interesting articles, frequently asked questions about research ethics.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe for research subjects.

You’ll balance pursuing important research objectives with using ethical research methods and procedures. It’s always necessary to prevent permanent or excessive harm to participants, whether inadvertent or not.

Defying research ethics will also lower the credibility of your research because it’s hard for others to trust your data if your methods are morally questionable.

Even if a research idea is valuable to society, it doesn’t justify violating the human rights or dignity of your study participants.

Prevent plagiarism. Run a free check.

Before you start any study involving data collection with people, you’ll submit your research proposal to an institutional review board (IRB) .

An IRB is a committee that checks whether your research aims and research design are ethically acceptable and follow your institution’s code of conduct. They check that your research materials and procedures are up to code.

If successful, you’ll receive IRB approval, and you can begin collecting data according to the approved procedures. If you want to make any changes to your procedures or materials, you’ll need to submit a modification application to the IRB for approval.

If unsuccessful, you may be asked to re-submit with modifications or your research proposal may receive a rejection. To get IRB approval, it’s important to explicitly note how you’ll tackle each of the ethical issues that may arise in your study.

There are several ethical issues you should always pay attention to in your research design, and these issues can overlap with each other.

You’ll usually outline ways you’ll deal with each issue in your research proposal if you plan to collect data from participants.

Voluntary participation means that all research subjects are free to choose to participate without any pressure or coercion.

All participants are able to withdraw from, or leave, the study at any point without feeling an obligation to continue. Your participants don’t need to provide a reason for leaving the study.

It’s important to make it clear to participants that there are no negative consequences or repercussions to their refusal to participate. After all, they’re taking the time to help you in the research process , so you should respect their decisions without trying to change their minds.

Voluntary participation is an ethical principle protected by international law and many scientific codes of conduct.

Take special care to ensure there’s no pressure on participants when you’re working with vulnerable groups of people who may find it hard to stop the study even when they want to.

Informed consent refers to a situation in which all potential participants receive and understand all the information they need to decide whether they want to participate. This includes information about the study’s benefits, risks, funding, and institutional approval.

You make sure to provide all potential participants with all the relevant information about

• what the study is about
• the risks and benefits of taking part
• how long the study will take
• your supervisor’s contact information and the institution’s approval number

Usually, you’ll provide participants with a text for them to read and ask them if they have any questions. If they agree to participate, they can sign or initial the consent form. Note that this may not be sufficient for informed consent when you work with particularly vulnerable groups of people.

If you’re collecting data from people with low literacy, make sure to verbally explain the consent form to them before they agree to participate.

For participants with very limited English proficiency, you should always translate the study materials or work with an interpreter so they have all the information in their first language.

In research with children, you’ll often need informed permission for their participation from their parents or guardians. Although children cannot give informed consent, it’s best to also ask for their assent (agreement) to participate, depending on their age and maturity level.

Anonymity means that you don’t know who the participants are and you can’t link any individual participant to their data.

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, and videos.

In many cases, it may be impossible to truly anonymize data collection . For example, data collected in person or by phone cannot be considered fully anonymous because some personal identifiers (demographic information or phone numbers) are impossible to hide.

You’ll also need to collect some identifying information if you give your participants the option to withdraw their data at a later stage.

Data pseudonymization is an alternative method where you replace identifying information about participants with pseudonymous, or fake, identifiers. The data can still be linked to participants but it’s harder to do so because you separate personal information from the study data.

Confidentiality means that you know who the participants are, but you remove all identifying information from your report.

All participants have a right to privacy, so you should protect their personal data for as long as you store or use it. Even when you can’t collect data anonymously, you should secure confidentiality whenever you can.

Some research designs aren’t conducive to confidentiality, but it’s important to make all attempts and inform participants of the risks involved.

As a researcher, you have to consider all possible sources of harm to participants. Harm can come in many different forms.

• Psychological harm: Sensitive questions or tasks may trigger negative emotions such as shame or anxiety.
• Social harm: Participation can involve social risks, public embarrassment, or stigma.
• Physical harm: Pain or injury can result from the study procedures.
• Legal harm: Reporting sensitive data could lead to legal risks or a breach of privacy.

It’s best to consider every possible source of harm in your study as well as concrete ways to mitigate them. Involve your supervisor to discuss steps for harm reduction.

Make sure to disclose all possible risks of harm to participants before the study to get informed consent. If there is a risk of harm, prepare to provide participants with resources or counseling or medical services if needed.

Some of these questions may bring up negative emotions, so you inform participants about the sensitive nature of the survey and assure them that their responses will be confidential.

The way you communicate your research results can sometimes involve ethical issues. Good science communication is honest, reliable, and credible. It’s best to make your results as transparent as possible.

Take steps to actively avoid plagiarism and research misconduct wherever possible.

Plagiarism means submitting others’ works as your own. Although it can be unintentional, copying someone else’s work without proper credit amounts to stealing. It’s an ethical problem in research communication because you may benefit by harming other researchers.

Self-plagiarism is when you republish or re-submit parts of your own papers or reports without properly citing your original work.

This is problematic because you may benefit from presenting your ideas as new and original even though they’ve already been published elsewhere in the past. You may also be infringing on your previous publisher’s copyright, violating an ethical code, or wasting time and resources by doing so.

In extreme cases of self-plagiarism, entire datasets or papers are sometimes duplicated. These are major ethical violations because they can skew research findings if taken as original data.

You notice that two published studies have similar characteristics even though they are from different years. Their sample sizes, locations, treatments, and results are highly similar, and the studies share one author in common.

Research misconduct

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement about data analyses.

Research misconduct is a serious ethical issue because it can undermine academic integrity and institutional credibility. It leads to a waste of funding and resources that could have been used for alternative research.

Later investigations revealed that they fabricated and manipulated their data to show a nonexistent link between vaccines and autism. Wakefield also neglected to disclose important conflicts of interest, and his medical license was taken away.

This fraudulent work sparked vaccine hesitancy among parents and caregivers. The rate of MMR vaccinations in children fell sharply, and measles outbreaks became more common due to a lack of herd immunity.

Research scandals with ethical failures are littered throughout history, but some took place not that long ago.

Some scientists in positions of power have historically mistreated or even abused research participants to investigate research problems at any cost. These participants were prisoners, under their care, or otherwise trusted them to treat them with dignity.

To demonstrate the importance of research ethics, we’ll briefly review two research studies that violated human rights in modern history.

These experiments were inhumane and resulted in trauma, permanent disabilities, or death in many cases.

After some Nazi doctors were put on trial for their crimes, the Nuremberg Code of research ethics for human experimentation was developed in 1947 to establish a new standard for human experimentation in medical research.

In reality, the actual goal was to study the effects of the disease when left untreated, and the researchers never informed participants about their diagnoses or the research aims.

Although participants experienced severe health problems, including blindness and other complications, the researchers only pretended to provide medical care.

When treatment became possible in 1943, 11 years after the study began, none of the participants were offered it, despite their health conditions and high risk of death.

Ethical failures like these resulted in severe harm to participants, wasted resources, and lower trust in science and scientists. This is why all research institutions have strict ethical guidelines for performing research.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias
• Social desirability bias

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

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Ethical Standards

Ethical standards for human services professionals, national organization for human services adopted 2024.

The field of human services is broadly defined, uniquely approaching the objective of meeting human needs through an interdisciplinary knowledge base, focusing on prevention as well as remediation of problems, and maintaining a commitment to improving the overall quality of life of service populations. The human services profession is one which promotes improved service delivery systems by addressing not only the quality of direct services, but also by seeking to improve accessibility, accountability, and coordination among professionals and agencies in service delivery.

Ethics Code Purpose :

The purpose of this ethics code is to establish a set of principles and standards to guide decision-making and conduct for all human services professionals. It serves to safeguard the well-being of clients, uphold the integrity of the profession, and foster trust and respect in all professional relationships. This code is designed to promote excellence in service delivery, ensure ethical practice in diverse social contexts, and address ethical dilemmas with professionalism and moral clarity. By adhering to this code, members commit to the highest standards of ethical behavior of their field which transcend legal requirements, foster enduring trust and respect, and advance the principles of human dignity, social justice, and responsible stewardship of the care placed in them by clients and society towards making profound contributions to their well-being.

Persons who use this code include members of the National Organization for Human Services, students in relevant academic degree programs, faculty in those same programs, researchers, administrators, practitioners, employers, supervisors, and others in community agencies who identify with the human services field (henceforth all are referred to throughout this code as human service professionals). The ethical standards are organized according to defined professional domains.

This ethics code emphasizes our unwavering commitment to ethical decision-making, a cornerstone of the human services profession. This commitment obliges all human service professionals to engage in continuous reflection, prioritize the welfare and rights of service recipients, and navigate complex ethical landscapes with diligence and moral clarity. It mandates the seeking of counsel and adherence to established ethical guidelines when confronting dilemmas, and ensures decisions are made with integrity and transparency.

Responsibility to Clients

STANDARD 1 Human service professionals recognize and build on client and community strengths.

STANDARD 2 At the beginning of the helping relationship, human service professionals obtain informed consent for services from clients. Clients should be informed that they may withdraw consent at any time and can ask questions before agreeing to the services. Clients who are unable to give consent should have those who are legally responsible for them review an informed consent statement document and provide appropriate consent. In the case of mandated services, human service professionals explain to clients their right to consent, including limitations to confidentiality and privacy, and possible consequences from service refusal.

STANDARD 3 Human service professionals protect the client's right to privacy and confidentiality except when such confidentiality would cause serious harm to the client or others, when agency guidelines state otherwise, or under other stated conditions (e.g., local, state, or federal laws). Human service professionals inform clients of the limits of confidentiality prior to the onset of the helping relationship.

STANDARD 4 When a human service professional suspects a client's behavior may endanger themselves or others, they must take appropriate and professional actions to ensure safety, which may include consulting, seeking supervision, or, in accordance with state and federal laws, breaching confidentiality.

STANDARD 5 Human service professionals recognize the potential harm and impaired judgment resulting from dual or multiple relationships with clients. If such relationships cannot be avoided, professionals must assess whether to limit or forego the professional relationship and make appropriate referrals as needed.

STANDARD 6 Human service professionals must not engage in sexual or romantic relationships with current clients. In the case of former clients, their friends, or family, professionals must thoroughly assess any potential harm or exploitative dynamics before considering such relationships.

STANDARD 7 Human service professionals ensure that their values or biases are not imposed upon their clients.

STANDARD 8 Human service professionals are responsible for safeguarding the integrity, safety, and security of client records. Client information, whether in written or electronic form, can only be shared with other professionals with the client's prior written consent, unless required or allowed by law or during professional supervision.

STANDARD 9 When providing services through the use of technology, human service professionals take precautions to ensure and maintain confidentiality and comply with all relevant laws and requirements regarding storing, transmitting, and retrieving data. In addition, human service professionals ensure that clients are aware of any issues and concerns related to confidentiality, service issues, and how technology might negatively or positively impact the helping relationship.

Responsibility to the Public and Society

STANDARD 10 Human service professionals are committed to delivering services equitably, ensuring inclusion and accessibility, and respective diversity by valuing and affirming differences in age, ethnicity, culture, race, ability, gender, gender identity, gender expression, language preference, religion, sexual orientation, socioeconomic status, nationality, and other identities associated with historically marginalized groups.

STANDARD 11 Human service professionals are knowledgeable about their cultures and communities within which they practice. They are aware of diversity in society and its impact on the community as well as individuals within the community. They respect the cultures and beliefs of individuals and groups.

STANDARD 12 Human service professionals have a duty to be informed about relevant local, state, and federal laws. They bear the responsibility to advocate for amendments or changes in regulations and statutes when they conflict with the ethical standards and/or the rights of clients.

STANDARD 13 Human service professionals stay informed about current social issues as they affect clients and communities. If appropriate to the helping relationship, they share this information with clients, groups, and communities as part of their work.

STANDARD 14 Human service professionals are aware of social and political issues, comprehend their effects on clients, and recognize how the impact of such issues vary among individuals from diverse backgrounds.

STANDARD 15 Human service professionals must establish processes to identify client needs and assets, actively draw attention to them, and facilitate planning and advocacy at individual, community, and societal levels to address them.

STANDARD 16 Human service professionals advocate for social justice and seek to eliminate oppression. They raise awareness about systems of discrimination and inequity that affect historically minoritized and marginalized groups and advocate for systemic change to address these inequalities within their workplace, communities, and legislative systems.

STANDARD 17 Human service professionals accurately represent the effectiveness of their treatment programs, interventions, and techniques, substantiating claims with empirical data and/or sound theoretical inferences whenever feasible.

Responsibility to Colleagues

STANDARD 18 Human service professionals avoid duplicating another professional's helping relationship with a client. With written permission from their client, human service professionals consult with other professionals who are assisting the client in a different type of relationship when it is in the best interest of the client to do so.

STANDARD 19 When human service professionals have a conflict with a colleague, they first seek out the colleague to manage the problem. If this effort fails, the professional then seeks the assistance of supervisors, consultants, or other professionals in efforts to address the conflict.

STANDARD 20 Human service professionals have a duty to respond appropriately to unethical behavior of colleagues. This generally means first discussing the issue directly with the colleague in question, unless there are extenuating circumstances, such as concerns about repercussions due to an existing power relationship. If a satisfactory resolution is not reached or extenuating circumstances exist, the professional should report the colleague's behavior to a supervisor. If the potential unethical behavior is seen as egregious, then the individual should report the behavior to the colleague’s professional association, credentialing board, police, and/or other administrators or boards, as appropriate.

STANDARD 21 Clients have a right to information discussed between their helper and other professionals, except when state or federal law notes otherwise. All information discussed among professionals should be kept confidential from all others, except when withholding information would lead to harm to clients, others, or communities.

Responsibility to Employers

STANDARD 22 Human service professionals honor their commitments to employers to the fullest extent possible.

STANDARD 23 Human service professionals participate in efforts to establish and maintain employment conditions which are conducive to high quality client services. Whenever possible, they assist in evaluating the effectiveness of the agency through reliable and valid assessment measures.

STANDARD 24 When a conflict arises between fulfilling the responsibility to the employer and the responsibility to the client, human service professionals work with all involved to manage the conflict.

Responsibility to the Profession

STANDARD 25 Human service professionals seek the training, experience, education, and supervision necessary to ensure their effectiveness in working with culturally diverse individuals, families, and communities.

STANDARD 26 Human service professionals provide services only within their expertise and scope of practice, recognizing the limits to their knowledge and skills.

STANDARD 27 Human service professionals must accurately represent their

qualifications, encompassing, but not limited to, their skills, education, credentials, training,

and areas of expertise, to the client, colleagues, and members of the public. When any intentional or accidental misrepresentation is discovered, they must take immediate action to rectify the situation.

STANDARD 28 Human service professionals must pursue relevant consultation and supervision to guide their decision-making in the face of legal, ethical, or other complex dilemmas.

STANDARD 29 Human service professionals promote cooperation across related disciplines, aiming to enhance professional development and maximize the benefits of inter-professional collaboration for clients at all levels.

STANDARD 30 Human service professionals are committed to the continued development of the field. They promote professional association memberships, support research initiatives, foster educational advancement, advocate for appropriate legislative actions, and engage in other professional development activities.

STANDARD 31 Human service professionals continually seek out new and effective approaches to enhance their professional abilities and use techniques that are evidence-based. When practicing techniques that are experimental or new, they inform clients of the status of such techniques as well as the possible risks and gain client consent for their usage.

STANDARD 32 Human service professionals engage in research that upholds ethical standards, meets institutional standards, and maintains scientific integrity. Such research accounts for cross-cultural and diversity bias and is reported with transparency regarding its limitations.

STANDARD 33 Human service professionals exercise discretion in sharing personal information on social media, knowing that they reflect the profession of human services. They also reflect on how their public behavior might impact their personal reputation and the perception of their professional community.

Responsibility to Self

STANDARD 34 Human service professionals maintain awareness of their own cultural and diverse backgrounds, beliefs, values, and biases. They recognize the potential impact of these factors on their relationships with others and commit to delivering culturally competent services to all clients.

STANDARD 35 Human service professionals are committed to their personal growth and well-being to ensure they provide the highest quality service to clients. Should they become aware of any physical, emotional, or psychological impediments to their ability to serve clients effectively, they direct clients to appropriate alternative services and seek measures for personal remediation of such impediments via consultation, treatment, or education.

STANDARD 36 Human service professionals are dedicated to lifelong learning, consistently seeking to enhance their knowledge and skills to better serve their clients.

Responsibility to Students

STANDARD 37 Human service educators develop and implement teaching methodologies that are culturally sensitive, using pedagogical approaches that recognize student’s diverse backgrounds and perspectives.

STANDARD 38 Human service educators commit to fostering equity and inclusion, actively employing measures to ensure educational accessibility for students of all abilities.

STANDARD 39 Human service educators uphold high standards of scholarship in their academic, pedagogical, and professional engagements. They remain abreast of developments in the field through active participation in professional associations, attendance at workshops and conferences, and engagement in both the review and conduct of research.

STANDARD 40 Human service educators recognize and acknowledge the contributions of students to their work including, but not limited to, case material, grants, workshops, research, publications, and other related activities.

STANDARD 41 Human service educators monitor students' field experiences to ensure the quality of the placement site, the supervisory experience, and that the educational outcomes align with the objectives of personal, professional, academic, career, and civic development of the student. When students experience potentially harmful situations during field placements, educators provide appropriate investigations and respond as necessary to safeguard the student.

STANDARD 42 Human service educators establish and uphold appropriate guidelines regarding student disclosure of sensitive or personal information. This involves providing students with advance notice of any activities requiring self-disclosure, offering students the option to opt-out of in-depth self-disclosure when reasonable, and ensuring that there are processes in place to discuss and debrief these activities.

STANDARD 43 Human service educators acknowledge the inherent power imbalance in their relationships with students and are committed to defining and upholding clear ethical and professional boundaries. This commitment includes avoiding any behavior that is demeaning, embarrassing, or exploitative. Human service educators are dedicated to treating all students with fairness and equity, actively working to eliminate discrimination in all forms, and ensuring a respectful and inclusive educational environment.

STANDARD 44 Human service educators ensure that students are familiar with, guided by, and held accountable to the ethical standards and policies established by their academic program or department, the specific guidelines outlined in the course syllabus by the instructor, the expectations of their advisors, and the Ethical Standards of Human Service Professionals.

Copyright © 2024 National Organization for Human Services. All rights reserved. Note: This document may be reproduced in its entirety without permission for non-commercial purposes only. Any reproduction, modification, distribution, transmission, or commercial use of this document, or any part thereof, without prior written permission is strictly prohibited.

For more information regarding Ethical Standard please email the NOHS Ethics Chair at [email protected] .

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PDF version of previously adopted Ethics Code 1996

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Genealogical ethics in the united states and the popularization of genealogical research in the digital age.

1. Introduction

2. genealogy and ethics, 3. sensitive issues and ethical considerations.

Content Warning : Materials in the Library of Virginia’s collections contain historical terms, phrases, and images that are offensive to modern readers. These include demeaning and dehumanizing references to race, ethnicity, and nationality; enslaved or free status; physical or mental ability; gender and sexual orientation ( Library of Virginia 2023 , https://lva-virginia.libguides.com/land-grants , accessed on 21 January 2024).

Some content (or its descriptions) found on this site may be harmful and difficult to view. These materials may be graphic or reflect personal biases. In some cases, they may conflict with strongly held cultural values, beliefs or restrictions. We provide access to these materials to preserve the historical record, but we do not endorse the attitudes, prejudices, or behaviors found within them ( Georgia Historic Newspapers 2023 , https://gahistoricnewspapers.galileo.usg.edu/search/advanced/ , accessed on 21 January 2024).

3.1. Case A: A Troubled Past

3.2. case b: a personal regret, 3.3. case c: a hidden secret, 3.4. common themes, 4. unexpected genetic discoveries and ethical considerations.

• First, is there an ethical responsibility to Mary in telling her story? Did she know that the father who reared her was not biologically related to her?
• Second, is there an ethical responsibility to Mary’s mother in telling this story? Unless further records are located, it is impossible to know the circumstances through which Mary was conceived. Might her mother have been raped? Might Mary’s conception have been the result of a clandestine relationship hidden from her family? Did the man refuse to marry her after learning she was pregnant? Or, might she have made an informed decision to marry someone else who would help her achieve a more stable and productive life? Or, perhaps, was she already involved in a relationship with the father who raised Mary, thus opening the possibility that she may not have known herself that he was Mary’s biological father? Without further evidence, we cannot know the answer to any of these questions.
• Third, is there an ethical responsibility to the father who reared Mary? Did he know he was not Mary’s biological father? If he did, he would seem to have made the conscious choice to marry a woman already with child and to have given his name to her. He recorded her name first in the family Bible, and he left to Mary—with whom he lived the final years of his life—a share of his estate that equaled all his other children. He likely married Mary’s mother before her pregnancy began to be noticeable. They had been married for five and a half months by the time Mary was born, and he played a prominent role in her life. Soon after Mary’s birth, the family moved about seventy miles westwards, into what was then Georgia’s most western frontier. Had any suspicions lingered in the neighborhood about Mary’s origins, they likely would have been removed by the family’s relocation, and they seem never to have crossed paths with Progenitor X, her offspring, and her extended family again.
• Fourth, is there an ethical responsibility to Mary’s descendants? In this particular case, Mary’s social and legal father’s family has maintained a strong sense of identity, including regular family gatherings emphasizing connections between his family and those of his two brothers as well as among descendants of his other children. This discovery would effectively remove Mary’s descendants from this still active family circle while at the same time perhaps creating tensions among the descendants of Mary’s mother herself, who might question the identification or feel some embarrassment at having these obscure details of their ancestors’ lives resurrected after more than two centuries.

5. Identity Confusion and Ethical Considerations

5.1. example a: william burk and william l. burk, 5.2. example b: william thomas ellerbee and thomas william ellerbee, 5.3. example c: two john hammacks or one, 5.4. common themes, 6. conclusions, informed consent statement, data availability statement, conflicts of interest.

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Knight, T.D. Genealogical Ethics in the United States and the Popularization of Genealogical Research in the Digital Age. Genealogy 2024 , 8 , 78. https://doi.org/10.3390/genealogy8030078

Knight TD. Genealogical Ethics in the United States and the Popularization of Genealogical Research in the Digital Age. Genealogy . 2024; 8(3):78. https://doi.org/10.3390/genealogy8030078

Knight, Thomas Daniel. 2024. "Genealogical Ethics in the United States and the Popularization of Genealogical Research in the Digital Age" Genealogy 8, no. 3: 78. https://doi.org/10.3390/genealogy8030078

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Introducing Apple’s On-Device and Server Foundation Models

At the 2024 Worldwide Developers Conference , we introduced Apple Intelligence, a personal intelligence system integrated deeply into iOS 18, iPadOS 18, and macOS Sequoia.

Apple Intelligence is comprised of multiple highly-capable generative models that are specialized for our users’ everyday tasks, and can adapt on the fly for their current activity. The foundation models built into Apple Intelligence have been fine-tuned for user experiences such as writing and refining text, prioritizing and summarizing notifications, creating playful images for conversations with family and friends, and taking in-app actions to simplify interactions across apps.

In the following overview, we will detail how two of these models — a ~3 billion parameter on-device language model, and a larger server-based language model available with Private Cloud Compute and running on Apple silicon servers — have been built and adapted to perform specialized tasks efficiently, accurately, and responsibly. These two foundation models are part of a larger family of generative models created by Apple to support users and developers; this includes a coding model to build intelligence into Xcode, as well as a diffusion model to help users express themselves visually, for example, in the Messages app. We look forward to sharing more information soon on this broader set of models.

Our Focus on Responsible AI Development

Apple Intelligence is designed with our core values at every step and built on a foundation of groundbreaking privacy innovations.

Additionally, we have created a set of Responsible AI principles to guide how we develop AI tools, as well as the models that underpin them:

• Empower users with intelligent tools : We identify areas where AI can be used responsibly to create tools for addressing specific user needs. We respect how our users choose to use these tools to accomplish their goals.
• Represent our users : We build deeply personal products with the goal of representing users around the globe authentically. We work continuously to avoid perpetuating stereotypes and systemic biases across our AI tools and models.
• Design with care : We take precautions at every stage of our process, including design, model training, feature development, and quality evaluation to identify how our AI tools may be misused or lead to potential harm. We will continuously and proactively improve our AI tools with the help of user feedback.
• Protect privacy : We protect our users' privacy with powerful on-device processing and groundbreaking infrastructure like Private Cloud Compute. We do not use our users' private personal data or user interactions when training our foundation models.

These principles are reflected throughout the architecture that enables Apple Intelligence, connects features and tools with specialized models, and scans inputs and outputs to provide each feature with the information needed to function responsibly.

In the remainder of this overview, we provide details on decisions such as: how we develop models that are highly capable, fast, and power-efficient; how we approach training these models; how our adapters are fine-tuned for specific user needs; and how we evaluate model performance for both helpfulness and unintended harm.

Pre-Training

Our foundation models are trained on Apple's AXLearn framework , an open-source project we released in 2023. It builds on top of JAX and XLA, and allows us to train the models with high efficiency and scalability on various training hardware and cloud platforms, including TPUs and both cloud and on-premise GPUs. We used a combination of data parallelism, tensor parallelism, sequence parallelism, and Fully Sharded Data Parallel (FSDP) to scale training along multiple dimensions such as data, model, and sequence length.

We train our foundation models on licensed data, including data selected to enhance specific features, as well as publicly available data collected by our web-crawler, AppleBot. Web publishers have the option to opt out of the use of their web content for Apple Intelligence training with a data usage control.

We never use our users’ private personal data or user interactions when training our foundation models, and we apply filters to remove personally identifiable information like social security and credit card numbers that are publicly available on the Internet. We also filter profanity and other low-quality content to prevent its inclusion in the training corpus. In addition to filtering, we perform data extraction, deduplication, and the application of a model-based classifier to identify high quality documents.

Post-Training

We find that data quality is essential to model success, so we utilize a hybrid data strategy in our training pipeline, incorporating both human-annotated and synthetic data, and conduct thorough data curation and filtering procedures. We have developed two novel algorithms in post-training: (1) a rejection sampling fine-tuning algorithm with teacher committee, and (2) a reinforcement learning from human feedback (RLHF) algorithm with mirror descent policy optimization and a leave-one-out advantage estimator. We find that these two algorithms lead to significant improvement in the model’s instruction-following quality.

Optimization

In addition to ensuring our generative models are highly capable, we have used a range of innovative techniques to optimize them on-device and on our private cloud for speed and efficiency. We have applied an extensive set of optimizations for both first token and extended token inference performance.

Both the on-device and server models use grouped-query-attention. We use shared input and output vocab embedding tables to reduce memory requirements and inference cost. These shared embedding tensors are mapped without duplications. The on-device model uses a vocab size of 49K, while the server model uses a vocab size of 100K, which includes additional language and technical tokens.

For on-device inference, we use low-bit palletization, a critical optimization technique that achieves the necessary memory, power, and performance requirements. To maintain model quality, we developed a new framework using LoRA adapters that incorporates a mixed 2-bit and 4-bit configuration strategy — averaging 3.5 bits-per-weight — to achieve the same accuracy as the uncompressed models.

Additionally, we use an interactive model latency and power analysis tool, Talaria , to better guide the bit rate selection for each operation. We also utilize activation quantization and embedding quantization, and have developed an approach to enable efficient Key-Value (KV) cache update on our neural engines.

With this set of optimizations, on iPhone 15 Pro we are able to reach time-to-first-token latency of about 0.6 millisecond per prompt token, and a generation rate of 30 tokens per second. Notably, this performance is attained before employing token speculation techniques, from which we see further enhancement on the token generation rate.

Model Adaptation

Our foundation models are fine-tuned for users’ everyday activities, and can dynamically specialize themselves on-the-fly for the task at hand. We utilize adapters, small neural network modules that can be plugged into various layers of the pre-trained model, to fine-tune our models for specific tasks. For our models we adapt the attention matrices, the attention projection matrix, and the fully connected layers in the point-wise feedforward networks for a suitable set of the decoding layers of the transformer architecture.

By fine-tuning only the adapter layers, the original parameters of the base pre-trained model remain unchanged, preserving the general knowledge of the model while tailoring the adapter layers to support specific tasks.

We represent the values of the adapter parameters using 16 bits, and for the ~3 billion parameter on-device model, the parameters for a rank 16 adapter typically require 10s of megabytes. The adapter models can be dynamically loaded, temporarily cached in memory, and swapped — giving our foundation model the ability to specialize itself on the fly for the task at hand while efficiently managing memory and guaranteeing the operating system's responsiveness.

To facilitate the training of the adapters, we created an efficient infrastructure that allows us to rapidly retrain, test, and deploy adapters when either the base model or the training data gets updated. The adapter parameters are initialized using the accuracy-recovery adapter introduced in the Optimization section.

Performance and Evaluation

Our focus is on delivering generative models that can enable users to communicate, work, express themselves, and get things done across their Apple products. When benchmarking our models, we focus on human evaluation as we find that these results are highly correlated to user experience in our products. We conducted performance evaluations on both feature-specific adapters and the foundation models.

To illustrate our approach, we look at how we evaluated our adapter for summarization. As product requirements for summaries of emails and notifications differ in subtle but important ways, we fine-tune accuracy-recovery low-rank (LoRA) adapters on top of the palletized model to meet these specific requirements. Our training data is based on synthetic summaries generated from bigger server models, filtered by a rejection sampling strategy that keeps only the high quality summaries.

To evaluate the product-specific summarization, we use a set of 750 responses carefully sampled for each use case. These evaluation datasets emphasize a diverse set of inputs that our product features are likely to face in production, and include a stratified mixture of single and stacked documents of varying content types and lengths. As product features, it was important to evaluate performance against datasets that are representative of real use cases. We find that our models with adapters generate better summaries than a comparable model.

As part of responsible development, we identified and evaluated specific risks inherent to summarization. For example, summaries occasionally remove important nuance or other details in ways that are undesirable. However, we found that the summarization adapter did not amplify sensitive content in over 99% of targeted adversarial examples. We continue to adversarially probe to identify unknown harms and expand our evaluations to help guide further improvements.

In addition to evaluating feature specific performance powered by foundation models and adapters, we evaluate both the on-device and server-based models’ general capabilities. We utilize a comprehensive evaluation set of real-world prompts to test the general model capabilities. These prompts are diverse across different difficulty levels and cover major categories such as brainstorming, classification, closed question answering, coding, extraction, mathematical reasoning, open question answering, rewriting, safety, summarization, and writing.

We compare our models with both open-source models (Phi-3, Gemma, Mistral, DBRX) and commercial models of comparable size (GPT-3.5-Turbo, GPT-4-Turbo) 1 . We find that our models are preferred by human graders over most comparable competitor models. On this benchmark, our on-device model, with ~3B parameters, outperforms larger models including Phi-3-mini, Mistral-7B, and Gemma-7B. Our server model compares favorably to DBRX-Instruct, Mixtral-8x22B, and GPT-3.5-Turbo while being highly efficient.

We use a set of diverse adversarial prompts to test the model performance on harmful content, sensitive topics, and factuality. We measure the violation rates of each model as evaluated by human graders on this evaluation set, with a lower number being desirable. Both the on-device and server models are robust when faced with adversarial prompts, achieving violation rates lower than open-source and commercial models.

Our models are preferred by human graders as safe and helpful over competitor models for these prompts. However, considering the broad capabilities of large language models, we understand the limitation of our safety benchmark. We are actively conducting both manual and automatic red-teaming with internal and external teams to continue evaluating our models' safety.

To further evaluate our models, we use the Instruction-Following Eval (IFEval) benchmark to compare their instruction-following capabilities with models of comparable size. The results suggest that both our on-device and server model follow detailed instructions better than the open-source and commercial models of comparable size.

We evaluate our models’ writing ability on our internal summarization and composition benchmarks, consisting of a variety of writing instructions. These results do not refer to our feature-specific adapter for summarization (seen in Figure 3 ), nor do we have an adapter focused on composition.

The Apple foundation models and adapters introduced at WWDC24 underlie Apple Intelligence, the new personal intelligence system that is integrated deeply into iPhone, iPad, and Mac, and enables powerful capabilities across language, images, actions, and personal context. Our models have been created with the purpose of helping users do everyday activities across their Apple products, and developed responsibly at every stage and guided by Apple’s core values. We look forward to sharing more information soon on our broader family of generative models, including language, diffusion, and coding models.

[1] We compared against the following model versions: gpt-3.5-turbo-0125, gpt-4-0125-preview, Phi-3-mini-4k-instruct, Mistral-7B-Instruct-v0.2, Mixtral-8x22B-Instruct-v0.1, Gemma-1.1-2B, and Gemma-1.1-7B. The open-source and Apple models are evaluated in bfloat16 precision.

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A voice replicator is a powerful tool for people at risk of losing their ability to speak, including those with a recent diagnosis of amyotrophic lateral sclerosis (ALS) or other conditions that can progressively impact speaking ability. First introduced in May 2023 and made available on iOS 17 in September 2023, Personal Voice is a tool that creates a synthesized voice for such users to speak in FaceTime, phone calls, assistive communication apps, and in-person conversations.

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In this post, we share highlights from workshop discussions and recordings of select workshop talks.

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