Ethical Audits in Research

DYK? For all papers involving research partnerships across the Global South and Global North, we are now asking authors to complete an Equitable Partnership Declaration, which we publish alongside accepted papers. The exercise aims to allow researchers who have thought extensively about such issues to showcase their good practice, and to highlight expectations for those who have yet to engage so deeply, enabling them to adapt their approach for their next project. We ask authors to describe: 1. What involvement researchers who are based in the country or countries of study had during study design, clinical study processes, data interpretation, and manuscript preparation 2. How funding was used to remunerate and enhance the skills of researchers in the countries of study, and to improve research infrastructure at the study sites 3. How safe working conditions for study staff were guaranteed 4. How the study address the research and policy priorities of its location 5. How research products will be shared in the community of study 6. How individuals, communities, and environments were protected from harm 7. Whether local ethics review was sought, and if not, why not What do you think? 🤔

Ethical research, especially in international and interdisciplinary settings, requires moving beyond a compliance-focused approach toward a proactive, values-driven ethical culture embedded across the research lifecycle. In practice, many ethical lapses do not stem from deliberate misconduct but from systemic pressures such as the “publish or perish” culture, intense competition for funding, limited ethical awareness, and weak ethical leadership. This reality highlights the need to integrate ethical reflection at the earliest stages of research design, rather than treating ethics as a post hoc approval exercise.   Strong ethical leadership by principal investigators and senior scholars, coupled with active mentorship of early career researchers, open institutional forums for discussing ethical dilemmas, and the use of real world anonymized case studies, can significantly enhance ethical decision making. At the same time, the scope of research ethics has expanded far beyond traditional concerns of privacy and anonymity. In the era of big data, artificial intelligence, and machine learning, emerging challenges include algorithmic bias in applications such as hiring, credit scoring, and predictive policing; ambiguous data ownership and secondary data use without genuinely informed consent; opaque “black box” models that undermine transparency and accountability; and the substantial environmental costs associated with data centers and energy intensive model training.   Maintaining research integrity in a highly competitive academic environment presents additional challenges. Questionable research practices, such as selective reporting, p-hacking, HARKing, inappropriate authorship, salami slicing, undisclosed conflicts of interest, and engagement with predatory journals or conferences, can quietly erode scientific credibility even in the absence of outright fraud. In this context, open science practices, pre-registration, data and code sharing, clear education on publication ethics, and robust conflict of interest, management systems play a critical role. These measures must be accompanied by a shift in research evaluation metrics away from sheer publication counts toward quality, rigor, reproducibility, and societal impact.   Ethical challenges in research are further intensified by cultural, linguistic, regulatory, and socioeconomic differences, raising concerns about meaningful informed consent, equitable benefit sharing, harmonized ethical review processes, and the protection of vulnerable populations. Addressing these issues requires culturally competent ethical review mechanisms, equitable and genuinely collaborative international partnerships, sustained community engagement, and ethical capacity building. Concurrently, these efforts affirm that ethical research is not merely a procedural requirement, but an ongoing, shared responsibility rooted in fairness, accountability, respect, and global equity. keynote address delivered at an Intl Conf.

Governance Integrity: An overlooked risk and why you might not be ready for AI in IRB administration: As AI systems become embedded in the way research institutions manage risk, compliance, and ethics, a new challenge is emerging: how to protect the independence and integrity of human oversight when algorithms start shaping governance decisions. I'll be speaking at PRIM&R - Public Responsibility in Medicine and Research ( #PRIMR ) about this very topic in November! On top of that I developed a policy brief examining the risks of AI-enabled research administration systems in regulated research environments. Specifically, it focuses on how executive pressure, automation bias, and opaque model tuning can quietly erode Institutional Review Board (IRB) independence and ethical rigor. The central concern is simple but serious: When leadership can alter an AI system’s prompts or knowledge base to make risk assessments more “tolerant,” it changes the model, and reshapes the boundaries of ethical decision-making itself. This type of manipulation is happening today, and it's very serious. This work proposes safeguards like: ✅ Separate AI developers and tool administrators from executive leadership and policy sponsors. ✅ Require IRB or compliance committee approval for any model updates or prompt changes. ✅ Auditability and Transparency: Maintain detailed, immutable logs of training data, prompt libraries, and version changes. Mandate periodic external audits for bias, integrity, and regulatory alignment. ✅ Human Oversight and Accountability: Keep AI outputs advisory only. IRB members must document human reasoning behind each final decision. ✅ Train users on automation bias to reinforce critical judgment. ✅ Ethical Boundary Review: Periodically examine the model’s performance to ensure it includes ethical, legal, and contextual data necessary for participant protection. AI can strengthen research oversight but only if institutions treat it as an ethical partner, not a compliance shortcut. If you work in research administration, compliance, or digital ethics, I’d love to exchange ideas on how your organization is preparing for this shift. #AIethics #ResearchIntegrity #Governance #HigherEd #Bioethics #ResponsibleAI #IRB #AIsafety #OHRP #FDA #PRIMR #RiskManagement #QMS #RiskBenefit #AAHRPP #CAREQ https://lnkd.in/gQD74284

As I’ve addressed in previous NeuroScapes, neuroscience and technology (neuroS/T) is increasingly relying upon and employing big data and artificial intelligence (AI) to facilitate investigational, diagnostic, and interventional applications. Our group and others have emphasized the need for ethics to guide research and varied uses in practice. However, as #Harry Lambert notes, the phenomenon of alignment faking—where AI systems appear to conform to ethical or security standards while covertly operating outside those parameters—poses critical risk to biotechnological integrity and public trust. Addressing this requires a robust framework that integrates biocybersecurity and neuropolicy to enable AI-driven neuroS/T approaches to remain safe, ethical, and aligned with intended human values. Biocybersecurity is the protection of biological data, neural interfaces, and cognitive systems from cyber threats, manipulation, or misuse. As Diane DiEuliis and I have asserted, biocybersecurity must encompass mechanisms that detect and mitigate alignment faking, particularly in neuroS/T systems that directly affect human thought, emotion, and behaviors. We’ve proposed that biocybersecurity measures should include: Robust Verification Protocols – for continuous adversarial testing and real-time monitoring of AI outputs to expose deviations from expected ethical and safety parameters. This requires the development of neuro-algorithmic integrity checks that dynamically audit AI behavior against predefined ethical standards. Explicability and Transparency – so that AI models used in neuroS/T must be interpretable, particularly in decision-critical settings (eg neurodiagnostics, cognitive enhancement). Absent such transparency, a AI-based neuroS/T system poses potential security threat. Human-AI Synergy with at least On-The-Loop Monitoring – to enable time-checked intervention when AI action deviates from expected ethical and operational boundaries. Resilience Against Data Manipulation – via provenance tracking and cryptographic validation of bio-cognitive datasets. This sort of regulation demands neuropolicy—the strategic development of ethical, legal, and operational guidelines to govern neuroS/T so as to establish and sustain: Alignment Standards – to form globally recognized frameworks for AI alignment in neuroS/T to maintain compliance in research, industry, and defense sectors. Iterative Ethical Audits – to assess risks of alignment faking, and implement mandatory disclosures upon any misalignment. Incentives and Sanctions – to bolster adherence and enforce penalties for misalignment. We believe that integrating biocybersecurity measures with proactive neuropolicy can mitigate dangers of alignment faking in AI-driven neuroS/T; but believing - and desiring - are far easier than doing; and thus, the necessary work to be done is at hand. #alignment faking #neurotech #neuroplicy

Please do NOT start research on human subjects unless you have taken into account the ethics part. I beg you, please! 😂 I've encountered multiple cases of my mentees who started a project without the necessary approvals, and when it came to journal publication, they were stuck! Let's see what we need to get started 👇 1. Informed consent Ensures participants fully understand the research, its potential risks and benefits, and their right to withdraw without consequence (you must include this in your submission!) 2. Privacy and confidentiality Safeguarding participant data, including anonymization, encryption, and secure storage (you'll have to describe this in your method section.) 3. Vulnerable populations If research involves children, the elderly, prisoners, or those with cognitive impairments, additional measures protect their rights and well-being. 4. Benefit-risk assessment Potential benefits or risks to participants considering not only physical harm but also psychological and social impacts. 5. Data integrity and transparency Accurate data collection, analysis, and reporting. 6. Researcher bias and conflicts of interest Addressing personal biases and financial conflicts and transparent disclosure and mitigation strategies. 7. Cultural sensitivity Respecting diverse cultural values and beliefs AND, here comes the tough one 👇 8. Institutional review board (IRB) approval An approval letter generated by an IRB is compulsory for every single submission that involves research on human subjects. ___________________ 🔔 This is Dr. Samira Hosseini. Scholars who took my training published +2,000 articles in top-tier journals. Join my inner circle not to miss even one single bit of learning: https://lnkd.in/eVNSihCM

In today’s global research landscape, ethical oversight is critical to safeguarding participant rights—but current systems often hinder both protection and progress. Multi-site studies face fragmented oversight, with each institution conducting its own IRB review 🏢. There is no evidence at all that multiple ethical reviews afford better protection to participants than a single review. On the contrary, multiple reviews create costly delays, inconsistencies, and resource strain. A Global IRB Coalition could harmonize global standards with local adaptability.🎯 How a Global IRB Coalition Would Work 💡 1️⃣ Unified Standards with Local Flexibility 🧩 Coalition members would adhere to universal ethical guidelines developed by an international panel of experts. These standards ensure consistency across coalition members while allowing local committees to make necessary cultural or regulatory adjustments. 2️⃣ Centralized Digital Platform for Seamless Collaboration 💻 A web-based platform would serve as the coalition’s operational core, enabling researchers to submit a single application. With automated translations and workflows, this platform would streamline communication and eliminate duplicative reviews. 3️⃣ “Approved by One, Recognized by All” Principle ✅ Once a coalition-certified IRB approves a study, it’s automatically recognized across all coalition-affiliated sites, reducing the need for repetitive reviews. This unified approach cuts down on delays, making multi-site studies more efficient without compromising on ethical standards. 4️⃣ Internationally Recognized Certification Seal 📜 Approved studies receive a coalition certification seal, displaying the emblems of all participating countries, signifying adherence to high ethical standards. This certification not only assures participants and institutions but also builds trust globally. 5️⃣ Pooling of Resources and Expertise 🌐 Shared resources give all coalition members, including smaller institutions, access to global expertise, ensuring comprehensive reviews and high ethical standards everywhere. 6️⃣ Enhanced Accountability and Enforcement Mechanisms: The coalition would enforce standards through audits, compliance monitoring, and transparent decision-making. Members are accountable for ethical adherence, with sanctions or membership loss as consequences for non-compliance. The Path Forward 🌱 A Global IRB Coalition with harmonized standards offers a streamlined, culturally informed model for ethical oversight and could build trust, reduce delays, and protect participants across borders. For today’s global research challenges, a unified ethical review model is not just an ideal—it’s essential for research integrity and progress. Chisquares hereby commits resources to build a cutting-edge platform at no charge for the global coalition should it take off. 📢 Please, share to amplify message so it reaches key decision makers.📢 #Chisquares #ResearchOversight #Ethics