Research Ethics Board Procedures

📢 The World Health Organization's Genomics Programme has officially launched the new principles for ethical human genome data collection, access, use, and sharing! 🌍🧬 These guidelines set a global standard for responsible genomic data practices, developed with input from the WHO Technical Advisory Group on Genomics (TAG-G) and international experts. As genomic technologies continue to advance, it’s crucial to have a robust framework to: 🔍 Ensure Informed Consent and Privacy Protect individual rights by promoting transparency and clear communication about data use. 🤝 Promote Equity and Inclusivity Address disparities in genomic research and ensure fair representation of diverse populations, especially from low- and middle-income countries (LMICs). 🌐 Foster Global Collaboration Encourage partnerships across borders and sectors to maximize the benefits of genomic data sharing, while upholding strict standards for privacy and security. 💡 Support Capacity Building Strengthen local infrastructure and enhance genomic literacy to make genomic data practices more inclusive and sustainable worldwide. These principles aim to guide researchers, policymakers, and healthcare providers in aligning their practices with WHO’s commitment to ethical genomics. The document provides actionable recommendations to address the key ethical, social, and legal challenges in the field. 📥 Ready to dive in? Download the full document here: https://lnkd.in/eNQpxNn2 🗓️ Stay tuned for an upcoming webinar to learn more about these new guidelines and how they can be applied in practice. #Genomics #GlobalHealth #Equity #DataEthics #Collaboration #WHO #Research Sara Niedbalski, Ph.D. Sergio Carmona Ciara Staunton Elena Ambrosino raffaella casolino Zilfalil Alwi Mascalzoni Deborah Tiffany Boughtwood Marc Abramowicz Michele Ramsay Gabriela Repetto Ahmad Abou Tayoun, PhD, FACMG Iscia Lopes-Cendes Yosr Hamdi Kazuto Kato Sherry Taylor PhD, FCCMG, ErCLG Tim Hubbard Rokhaya Ndiaye

The Future of Privacy Regulations and Marketing Introduction & Overview As consumers demand greater control over personal data, businesses face the challenge of adapting to privacy regulations like GDPR and CCPA, which aim to enhance transparency but complicate marketing efforts. This article explores the impact of emerging privacy regulations on marketing and outlines strategies for businesses to prepare for a data-privacy-driven future. What Are Privacy Regulations? Privacy regulations are laws that govern the collection, storage, and use of consumer data to ensure it is handled responsibly. Laws like GDPR (EU) and CCPA (California) enforce strict data protection standards, granting consumers control over their data and imposing fines for non-compliance. The Growing Importance of Data Privacy In 2024, data privacy is a top priority. With rising data breaches, consumers are concerned about data misuse, pushing governments to enforce stricter regulations to protect personal information and promote transparency. Key Regulations: GDPR and CCPA GDPR: Enforced in 2018, GDPR requires companies to obtain explicit consent and securely handle EU citizens' data, with penalties for breaches. CCPA: Effective since 2020, CCPA allows California residents to know what data is collected, request deletion, and opt out of data sales. Challenges Navigating privacy laws is complex and costly, requiring investment in secure data systems and legal resources. Compliance restricts data collection, impacting targeted marketing, and failure to comply risks severe fines, like up to €20 million or 4% of global revenue under GDPR. Strategies & Solutions To comply, businesses should audit data, update privacy policies, secure user consent, limit data collection, and train employees on privacy best practices. Marketers can adapt by focusing on first-party data, using contextual targeting, and adopting consent-based marketing. Benefits & Insights Privacy compliance strengthens consumer trust, boosts brand reputation, and improves data quality. Transparent practices foster customer loyalty, while using first-party data enhances marketing effectiveness and insights. Conclusion & Next Steps As privacy regulations evolve, businesses must prioritize compliance through regular audits, updated privacy policies, and robust security. Embracing privacy can build trust and drive growth, turning regulatory challenges into opportunities. Next steps include refining data practices and adopting privacy-centric marketing strategies. #PrivacyRegulations #MarketingTrends #DataProtection #DigitalPrivacy #ConsumerTrust #ComplianceMatters #DataSecurity #PersonalData #MarketingStrategies

✳ Bridging Ethics and Operations in AI Systems✳ Governance for AI systems needs to balance operational goals with ethical considerations. #ISO5339 and #ISO24368 provide practical tools for embedding ethics into the development and management of AI systems. ➡Connecting ISO5339 to Ethical Operations  ISO5339 offers detailed guidance for integrating ethical principles into AI workflows. It focuses on creating systems that are responsive to the people and communities they affect. 1. Engaging Stakeholders  Stakeholders impacted by AI systems often bring perspectives that developers may overlook. ISO5339 emphasizes working with users, affected communities, and industry partners to uncover potential risks and ensure systems are designed with real-world impact in mind. 2. Ensuring Transparency  AI systems must be explainable to maintain trust. ISO5339 recommends designing systems that can communicate how decisions are made in a way that non-technical users can understand. This is especially critical in areas where decisions directly affect lives, such as healthcare or hiring. 3. Evaluating Bias  Bias in AI systems often arises from incomplete data or unintended algorithmic behaviors. ISO5339 supports ongoing evaluations to identify and address these issues during development and deployment, reducing the likelihood of harm. ➡Expanding on Ethics with ISO24368  ISO24368 provides a broader view of the societal and ethical challenges of AI, offering additional guidance for long-term accountability and fairness. ✅Fairness: AI systems can unintentionally reinforce existing inequalities. ISO24368 emphasizes assessing decisions to prevent discriminatory impacts and to align outcomes with social expectations.  ✅Transparency: Systems that operate without clarity risk losing user trust. ISO24368 highlights the importance of creating processes where decision-making paths are fully traceable and understandable.  ✅Human Accountability: Decisions made by AI should remain subject to human review. ISO24368 stresses the need for mechanisms that allow organizations to take responsibility for outcomes and override decisions when necessary. ➡Applying These Standards in Practice  Ethical considerations cannot be separated from operational processes. ISO24368 encourages organizations to incorporate ethical reviews and risk assessments at each stage of the AI lifecycle. ISO5339 focuses on embedding these principles during system design, ensuring that ethics is part of both the foundation and the long-term management of AI systems. ➡Lessons from #EthicalMachines  In "Ethical Machines", Reid Blackman, Ph.D. highlights the importance of making ethics practical. He argues for actionable frameworks that ensure AI systems are designed to meet societal expectations and business goals. Blackman’s focus on stakeholder input, decision transparency, and accountability closely aligns with the goals of ISO5339 and ISO24368, providing a clear way forward for organizations.

In a data-driven world, considering ethical implications is a responsibility for all kinds of data jobs. Here are the ethical considerations you will face: 1. 𝗗𝗮𝘁𝗮 𝗣𝗿𝗶𝘃𝗮𝗰𝘆: While collecting and analyzing data, you need to respect individual privacy. Anonymize data whenever possible and ensure compliance with regulations like GDPR.     2. 𝗕𝗶𝗮𝘀 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗠𝗶𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻: Algorithms are only as unbiased as the data they're trained on. Actively seek out and correct biases in your datasets to prevent promoting stereotypes or unfair treatment.     3. 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆: Be open about the methods, assumptions, and limitations of your work. Transparency builds trust, particularly when your analysis influences decision-making.     4. 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: Double-check your findings, validate your models, and always question the reliability of your sources.     5. 𝗜𝗺𝗽𝗮𝗰𝘁 𝗔𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀: Consider the broader implications of your analysis. Could your work unintentionally harm individuals or communities?     6. 𝗖𝗼𝗻𝘀𝗲𝗻𝘁: Ensure that data is collected ethically, with consent where necessary. Using data without permission can breach trust and legal boundaries. Ethics in data is not only about adhering to rules, but about fostering a culture of responsibility, respect, and integrity. The impact of ignoring those topics can be significant for your company due to losing the trust of your customers or substantial legal penalties. As an analyst, you play an important role in upholding those ethical standards and protecting your business. How do you incorporate ethical considerations into your data analysis process? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #dataethics #ethics #dataprivacy

Personalised risk assessment leveraging IoT sensor technology and machine learning... These researchers developed an integrated monitoring system including sensors that measure potentially harmful agents like dust, noise, ultraviolet radiation, illuminance, temperature, humidity, and flammable gases. The data collected by these sensors was then processed using machine learning algorithms to provide real-time, personalised safety recommendations. The system tested comprised wearable monitoring devices, a server-based web application for employers, and a mobile application for workers. By integrating health histories of workers, such as common diseases and symptoms related to the monitored agents, the system generates actionable alerts. These alerts are suggested to help companies make informed decisions to protect their employees from environmental hazards, both in immediate situations and for long-term safety planning - ideally improving work design. The research was conducted in lab conditions but proved which type of machine learning can be applied to different hazardous agents and the researchers determined that models can be applied to other agents not tested. So what? My thoughts are that it is likely we will see more hyper-personalised risk assessments leveraging IoT sensors in the future; either wearable or strategically located in workplaces. We've been observing this trend for some time, but with the advancements in machine learning, we now have the opportunity to understand much better how several different hazardous agents interact with each other and therefore, ideally, we have the intelligence needed redesign workplaces for the better; and we can also support individuals who have pre-existing exposures or vulnerabilities to thrive in the workplace. You can access this open source research here: https://lnkd.in/ggZpGRFS Follow my profile and these hashtags for more: #SafetyTechResearch #SafetyInnovation #SafetyTech and #SafetyTechNews

As a lawyer who often dives deep into the world of data privacy, I want to delve into three critical aspects of data protection: A) Data Privacy This fundamental right has become increasingly crucial in our data-driven world. Key features include: -Consent and transparency: Organizations must clearly communicate how they collect, use, and share personal data. This often involves detailed privacy policies and consent mechanisms. -Data minimization: Companies should only collect data that's necessary for their stated purposes. This principle not only reduces risk but also simplifies compliance efforts. -Rights of data subjects: Under regulations like GDPR, individuals have rights such as access, rectification, erasure, and data portability. Organizations need robust processes to handle these requests. -Cross-border data transfers: With the invalidation of Privacy Shield and complexities around Standard Contractual Clauses, ensuring compliant data flows across borders requires careful legal navigation. B) Data Processing Agreements (DPAs) These contracts govern the relationship between data controllers and processors, ensuring regulatory compliance. They should include: -Scope of processing: DPAs must clearly define the types of data being processed and the specific purposes for which processing is allowed. -Subprocessor management: Controllers typically require the right to approve or object to any subprocessors, with processors obligated to flow down DPA requirements. -Data breach protocols: DPAs should specify timeframes for breach notification (often 24-72 hours) and outline the required content of such notifications, -Audit rights: Most DPAs now include provisions for audits and/or acceptance of third-party certifications like SOC II Type II or ISO 27001. C) Data Security These measures include: -Technical measures: This could involve encryption (both at rest and in transit), multi-factor authentication, and regular penetration testing. -Organizational measures: Beyond technical controls, this includes data protection impact assessments (DPIAs), appointing data protection officers where required, and maintaining records of processing activities. -Incident response plans: These should detail roles and responsibilities, communication protocols, and steps for containment, eradication, and recovery. -Regular assessments: This often involves annual security reviews, ongoing vulnerability scans, and updating security measures in response to evolving threats. These aren't just compliance checkboxes – they're the foundation of trust in the digital economy. They're the guardians of our digital identities, enabling the data-driven services we rely on while safeguarding our fundamental rights. Remember, in an era where data is often called the "new oil," knowledge of these concepts is critical for any organization handling personal data. #legaltech #innovation #law #business #learning

𝗛𝗲𝗿𝗲'𝘀 𝗺𝘆 𝟳-𝘀𝘁𝗲𝗽 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸 𝗳𝗼𝗿 𝗲𝗻𝘀𝘂𝗿𝗶𝗻𝗴 𝘀𝗺𝗼𝗼𝘁𝗵 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝘀𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝘁 𝗜'𝘃𝗲 𝗿𝗲𝗳𝗶𝗻𝗲𝗱 𝗼𝘃𝗲𝗿 𝘆𝗲𝗮𝗿𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗠𝗲𝗱𝗧𝗲𝗰𝗵 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝘀𝗽𝗮𝗰𝗲: 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗲𝗻𝗱 𝗶𝗻 𝗺𝗶𝗻𝗱 - 𝟭𝟴-𝟮𝟰 𝗺𝗼𝗻𝘁𝗵𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘀𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻 • Map your regulatory strategy to your commercial goals • Identify your regulatory pathway early (510(k), De Novo, PMA) • Build testing protocols based on predicate devices when applicable 𝟮. 𝗗𝗲𝘀𝗶𝗴𝗻 𝘆𝗼𝘂𝗿 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺 𝗳𝗼𝗿 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 • Implement ISO 13485 principles from day one • Focus on the 7 critical SOPs that impact submissions most • Avoid the common trap of documentation overload (I've seen startups with 200+ SOPs when 35-40 would suffice) 𝟯. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗺𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝘆 𝗯𝗲𝗳𝗼𝗿𝗲 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗻𝗴 • Pre-validate test methods with 3-5 pilot runs • Engage with testing labs that have FDA submission experience • Document protocol deviations properly (we found 63% of submissions get delayed due to inadequate deviation management) 𝟰. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗽𝗿𝗲-𝘀𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗺𝗲𝗲𝘁𝗶𝗻𝗴𝘀 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰𝗮𝗹𝗹𝘆 • Schedule Q-Sub meetings 9-12 months before planned submission • Prepare focused questions (limit to a few critical issues) • Follow up with written summaries within the allocated time 𝟱. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝘀𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻 "𝘄𝗮𝗿 𝗿𝗼𝗼𝗺" • Assemble cross-functional team (R&D, Clinical, Quality, Regulatory) • Create submission trackers with accountability metrics • Hold twice-weekly stand-ups in the 90 days before submission 𝟲. 𝗖𝗼𝗻𝗱𝘂𝗰𝘁 𝘁𝗵𝗶𝗿𝗱-𝗽𝗮𝗿𝘁𝘆 𝘀𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗿𝗲𝘃𝗶𝗲𝘄 • Have external experts review 100% of your technical documentation • Use submission management platforms like RADAR or MasterControl • Schedule review 45-60 days before planned submission date 𝟳. 𝗣𝗿𝗲𝗽𝗮𝗿𝗲 𝗳𝗼𝗿 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗿𝗲𝘃𝗶𝗲𝘄 • Anticipate FDA questions with "pre-mortem" analysis • Have subject matter experts on standby during review period • Create response templates for common deficiency categories I learned these lessons the hard way. Early in my career I worked at a company where we had three submissions rejected due to inconsistent test data formatting. Now we use standardized data presentation templates that have cut our Additional Information requests by 72%. 𝗧𝗔𝗞𝗘𝗔𝗪𝗔𝗬: Regulatory success is about methodical preparation and strategic execution. The companies that view regulatory as a strategic function rather than a compliance burden consistently outperform their peers in time-to-market by an average of 7 months If you're preparing for an FDA submission in the next 12-18 months, I'd be happy to share our pre-submission checklist. Just message me directly

The National Institute of Standards and Technology (NIST) team have spent over 12 months exploring how the key ethical research principles for biomedical and behavioral research with human subjects in the United States can be integrated into AI research. One being that by obtaining informed consent from research participants and designing studies to minimize risks they can ensure transparency and protect individuals' data. Additionally, selecting subjects fairly and avoiding inappropriate exclusion can help address biases in AI datasets. It is important to note that the authors of this document emphasize thoughtfulness rather than advocating for more government regulation. By adopting these ethical principles voluntarily, companies can demonstrate their commitment to responsible AI development and usage. You can read this fascinating report here: https://lnkd.in/d7-t5e8d

When I post federal jobs, I often receive the question, "How should I apply?" The following are some guidelines that will help you on your quest to #jointheteam! 🎯 How to Apply for Federal Jobs on USAJobs — A Step-by-Step Guide Here’s a proven process to set yourself up for success: 1️⃣ Create Your USAJobs Account Go to USAJobs.gov and click “Create Profile”. Use a personal email address you’ll have long-term (not a work or .mil address). Fill out your profile completely — hiring managers use this information to match you to opportunities. 2️⃣ Build (or Upload) a Federal-Style Resume Federal resumes are very different from private sector resumes — they’re longer (3–5 pages is common) and detail-rich. Include: Job title, employer, and dates worked (month & year) for every position. Hours worked per week. Detailed duties and accomplishments linked to the job announcement’s qualifications. Use USAJobs Resume Builder to ensure correct formatting for HR systems. 3️⃣ Search and Save Jobs Use filters (location, agency, pay scale, series, telework options) to narrow results. Save searches with email alerts so new postings come to you — many roles close quickly. 4️⃣ Read the Job Announcement Carefully The “This job is open to” section tells you if you’re eligible (e.g., Veterans, Current Federal Employees, Open to the Public). The “Qualifications” section lists specialized experience requirements — your resume must clearly address these. 5️⃣ Prepare Your Application Package This usually includes: Federal resume Required documents (e.g., DD-214, SF-50, transcripts, certificates) Completed questionnaire Tip: Upload documents in PDF format to avoid formatting issues. 6️⃣ Apply Before the Closing Date Many postings close at 11:59 PM Eastern on the listed date. Don’t wait — USAJobs sometimes experiences high-traffic slowdowns. 7️⃣ Track Your Application Status Log in to USAJobs, go to “Applications”, and check the status updates: “Received” → “Reviewing Applications” → “Referred” (or “Not Referred”). Remember: “Not Referred” doesn’t mean you weren’t qualified — it may mean another candidate matched more closely. 8️⃣ Follow Up and Stay Persistent If possible, network with people in the hiring agency. Federal hiring can take months — keep applying to multiple postings. 💡 Pro Tip for Service Members & Veterans: Leverage your Veterans’ Preference and military-to-civilian skills translators to ensure your experience matches the job’s language. Small word changes can make a big difference in HR keyword scanning. ✅ Bottom Line: Applying on USAJobs is about attention to detail and persistence. The more closely your application matches the job announcement — in both language and documentation — the better your chances of being “Referred” for hiring consideration.

Risk Assessment. Risk assessment is “The process of quantifying the probability of a risk occurring and its likely impact on the project”. It is often undertaken, at least initially, on a qualitative basis by which I mean the use of a subjective method of assessment rather than a numerical or stochastic (probablistic) method. Such methods seek to assess risk to determine severity or exposure, recording the results in a probability and impact grid or ‘risk assessment matrix'. The infographic provides one example which usefully visually communicates the assessment to the project team and interested parties. Probability may be assessed using labels such as: Rare, unlikely, possible, likely and almost certain; whilst impact considered using labels: Insignificant, minor, medium, major and severe. Each label is assigned a ‘scale value’ or score with the values chosen to align with the risk appetite of the project and sponsoring organisation. The product of the scale values (i.e. probability x impact) resulting in a ranking index for each risk. Thresholds should be established early in the life cycle of the project for risk acceptance and risk escalation to aid decision-making and establish effetive governance principles. Risk assessment matrices are useful in the initial assessment of risk, providing a quick prioritisation of the project’s risk environment. It does not, however, give a full analysis of risk exposure that would be accomplished by quantitative risk analysis methods. Quantitative risk analysis may be defined as: “The estimation of numerical values of the probability and impact of risks on a project usually using actual or estimated values, known relationships between values, modelling, arithmetical and/or statistical techniques”. Quantitative methods assign a numerical value (e.g. 60%) to the probability of the risk occurring, where possible based on a verifiable data source. Impact is considered by means of more than one deterministic value (using at least 3-point estimation techniques) applying a distribution (uniform, normal or skewed) across the impact values. Quantitative risk methods provide a means of understanding how risk and uncertainty affect a project’s objectives and a view of its full risk exposure. It can also provide an assessment of the probability of achieving the planned schedule and cost estimate as well as a range of possible out-turns, helping to inform the provision of contingency reserves and time buffers. #projectmanagement #businesschange #roadmap