Data Ethics and Privacy Guidelines for Programmers

If you are an organisation using AI or you are an AI developer, the Australian privacy regulator has just published some vital information about AI and your privacy obligations. Here is a summary of the new guides for businesses published today by the Office of the Australian Information Commissioner which articulate how Australian privacy law applies to AI and set out the regulator’s expectations. The first guide is aimed to help businesses comply with their privacy obligations when using commercially available AI products and help them to select an appropriate product. The second provides privacy guidance to developers using personal information to train generative AI models. GUIDE ONE: Guidance on privacy and the use of commercially available AI products Top five takeaways * Privacy obligations will apply to any personal information input into an AI system, as well as the output data generated by AI (where it contains personal information).  * Businesses should update their privacy policies and notifications with clear and transparent information about their use of AI * If AI systems are used to generate or infer personal information, including images, this is a collection of personal information and must comply with APP 3 (which deals with collection of personal info). * If personal information is being input into an AI system, APP 6 requires entities to only use or disclose the information for the primary purpose for which it was collected. * As a matter of best practice, the OAIC recommends that organisations do not enter personal information, and particularly sensitive information, into publicly available generative AI tools. GUIDE 2: Guidance on privacy and developing and training generative AI models Top five takeaways * Developers must take reasonable steps to ensure accuracy in generative AI models. * Just because data is publicly available or otherwise accessible does not mean it can legally be used to train or fine-tune generative AI models or systems.. * Developers must take particular care with sensitive information, which generally requires consent to be collected. * Where developers are seeking to use personal information that they already hold for the purpose of training an AI model, and this was not a primary purpose of collection, they need to carefully consider their privacy obligations. * Where a developer cannot clearly establish that a secondary use for an AI-related purpose was within reasonable expectations and related to a primary purpose, to avoid regulatory risk they should seek consent for that use and/or offer individuals a meaningful and informed ability to opt-out of such a use. https://lnkd.in/gX_FrtS9

Fostering Responsible AI Use in Your Organization: A Blueprint for Ethical Innovation (here's a blueprint for responsible innovation) I always say your AI should be your ethical agent. In other words... You don't need to compromise ethics for innovation. Here's my (tried and tested) 7-step formula: 1. Establish Clear AI Ethics Guidelines ↳ Develop a comprehensive AI ethics policy ↳ Align it with your company values and industry standards ↳ Example: "Our AI must prioritize user privacy and data security" 2. Create an AI Ethics Committee ↳ Form a diverse team to oversee AI initiatives ↳ Include members from various departments and backgrounds ↳ Role: Review AI projects for ethical concerns and compliance 3. Implement Bias Detection and Mitigation ↳ Use tools to identify potential biases in AI systems ↳ Regularly audit AI outputs for fairness ↳ Action: Retrain models if biases are detected 4. Prioritize Transparency ↳ Clearly communicate how AI is used in your products/services ↳ Explain AI-driven decisions to affected stakeholders ↳ Principle: "No black box AI" - ensure explainability 5. Invest in AI Literacy Training ↳ Educate all employees on AI basics and ethical considerations ↳ Provide role-specific training on responsible AI use ↳ Goal: Create a culture of AI awareness and responsibility 6. Establish a Robust Data Governance Framework ↳ Implement strict data privacy and security measures ↳ Ensure compliance with regulations like GDPR, CCPA ↳ Practice: Regular data audits and access controls 7. Encourage Ethical Innovation ↳ Reward projects that demonstrate responsible AI use ↳ Include ethical considerations in AI project evaluations ↳ Motto: "Innovation with Integrity" Optimize your AI → Innovate responsibly

✳ Integrating AI, Privacy, and Information Security Governance ✳ Your approach to implementation should: 1. Define Your Strategic Context Begin by mapping out the internal and external factors impacting AI ethics, security, and privacy. Identify key regulations, stakeholder concerns, and organizational risks (ISO42001, Clause 4; ISO27001, Clause 4; ISO27701, Clause 5.2.1). Your goal should be to create unified objectives that address AI’s ethical impacts while maintaining data protection and privacy. 2. Establish a Multi-Faceted Policy Structure Policies need to reflect ethical AI use, secure data handling, and privacy safeguards. Ensure that policies clarify responsibilities for AI ethics, data security, and privacy management (ISO42001, Clause 5.2; ISO27001, Clause 5.2; ISO27701, Clause 5.3.2). Your top management must lead this effort, setting a clear tone that prioritizes both compliance and integrity across all systems (ISO42001, Clause 5.1; ISO27001, Clause 5.1; ISO27701, Clause 5.3.1). 3. Create an Integrated Risk Assessment Process Risk assessments should cover AI-specific threats (e.g., bias), security vulnerabilities (e.g., breaches), and privacy risks (e.g., PII exposure) simultaneously (ISO42001, Clause 6.1.2; ISO27001, Clause 6.1; ISO27701, Clause 5.4.1.2). By addressing these risks together, you can ensure a more comprehensive risk management plan that aligns with organizational priorities. 4. Develop Unified Controls and Documentation Documentation and controls must cover AI lifecycle management, data security, and privacy protection. Procedures must address ethical concerns and compliance requirements (ISO42001, Clause 7.5; ISO27001, Clause 7.5; ISO27701, Clause 5.5.5). Ensure that controls overlap, such as limiting access to AI systems to authorized users only, ensuring both security and ethical transparency (ISO27001, Annex A.9; ISO42001, Clause 8.1; ISO27701, Clause 5.6.3). 5. Coordinate Integrated Audits and Reviews Plan audits that evaluate compliance with AI ethics, data protection, and privacy principles together (ISO42001, Clause 9.2; ISO27001, Clause 9.2; ISO27701, Clause 5.7.2). During management reviews, analyze the performance of all integrated systems and identify improvements (ISO42001, Clause 9.3; ISO27001, Clause 9.3; ISO27701, Clause 5.7.3). 6. Leverage Technology to Support Integration Use GRC tools to manage risks across AI, information security, and privacy. Integrate AI for anomaly detection, breach prevention, and privacy safeguards (ISO42001, Clause 8.1; ISO27001, Annex A.14; ISO27701, Clause 5.6). 7. Foster an Organizational Culture of Ethics, Security, and Privacy Training programs must address ethical AI use, secure data handling, and privacy rights simultaneously (ISO42001, Clause 7.3; ISO27001, Clause 7.2; ISO27701, Clause 5.5.3). Encourage a mindset where employees actively integrate ethics, security, and privacy into their roles (ISO27701, Clause 5.5.4).

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

🚩 You Might Be Breaking Data Ethics Without Knowing It. Yes, I’ve Been a Victim Too. A few weeks ago, I shared a post about data ethics and confidentiality—something we rarely talk about but really should. Since then, I’ve received several DMs like: → “I didn’t even realize I was breaking these rules until I read your post.” → “Can you please write more about this? People need to be aware!.” So, I decided to bring it up again—for educational purposes to "whom it may concern." In today’s world where data is everywhere Handling it the right way isn’t just a “nice-to-have skill”—it’s part of our responsibility. Whether you’re: → Cleaning data → Building models → Sharing insights Remember: every row and type of data represents a real person. And with that comes with trust, privacy, and responsibility. 📍 Here are some key principles every data professional should keep in mind: 📌 Informed Consent → Ensure individuals understand how their data will be used. → Avoid hidden clauses—Just be clear and honest. 📌 Respect Privacy → Just because you can access some data doesn’t mean you should use it anyhow. → Know what's sensitive, and use it carefully. 📌 Think Ethically → Ask yourself: “Would I be comfortable if my data were used this way?” → Build that mindset into every step of your data work and avoid unethical use cases. 📌 Anonymize where you can → If the data doesn’t need to show personal details, mask or anonymize it. → There are tools and techniques to help with that—use them. 📌 Regulatory Compliance → Stay updated on laws like GDPR, CCPA, HIPAA, and other industry-specific regulations. → Conduct regular audits to ensure compliance. 📍Note: Data ethics isn’t just about compliance—it’s about doing the right thing. ❓ What data ethical rules have you broken unaware before? Kindly share ♻️ Repost for someone to learn and be aware. #VictorOmoboye #DataEthics

People data is probably the most sensitive data AI can have access to. Here are the Five Ps of Ethical Data Handling, according to HBR 👇 ➡️ Provenance • Where does the data come from? • Was it legally acquired? • Was appropriate consent obtained? ➡️ Purpose • Is the data being repurposed? • Would the original source of the data agree to its reuse for a purpose different from the one originally announced or implied? • If dark data is being used, will it remain within the parameters of its original collection mandates? ➡️ Protection • How is the data being protected? • How long will it be available for the project? • Who is responsible for destroying it? ➡️ Privacy • Who will have access to data that can be used to identify a person? • How will individual observations in the data set be anonymized? • Who will have access to anonymized data? ➡️ Preparation • How was the data cleaned? • Are data sets being combined in a way that preserves anonymity? • How is the accuracy of the data being verified and, if necessary, improved? • How are missing data and variables being managed? --- I'd recommend reading the full article by Michael Segalla and Dominique Rouziès which fleshes out each category with examples and nuance (link in the comments). There's plenty of relevance to L&D and its future integration into the wider HR tech and business intelligence ecosystem. #People #Data #Ethics #ArtificialIntelligence #HumanResources