Image Recognition Ethics In AI Development

🧭Governing AI Ethics with ISO42001🧭 Many organizations treat AI ethics as a branding exercise, a list of principles with no operational enforcement. As Reid Blackman, Ph.D. argues in "Ethical Machines", without governance structures, ethical commitments are empty promises. For those who prefer to create something different, #ISO42001 provides a practical framework to ensure AI ethics is embedded in real-world decision-making. ➡️Building Ethical AI with ISO42001 1. Define AI Ethics as a Business Priority ISO42001 requires organizations to formalize AI governance (Clause 5.2). This means: 🔸Establishing an AI policy linked to business strategy and compliance. 🔸Assigning clear leadership roles for AI oversight (Clause A.3.2). 🔸Aligning AI governance with existing security and risk frameworks (Clause A.2.3). 👉Without defined governance structures, AI ethics remains a concept, not a practice. 2. Conduct AI Risk & Impact Assessments Ethical failures often stem from hidden risks: bias in training data, misaligned incentives, unintended consequences. ISO42001 mandates: 🔸AI Risk Assessments (#ISO23894, Clause 6.1.2): Identifying bias, drift, and security vulnerabilities. 🔸AI Impact Assessments (#ISO42005, Clause 6.1.4): Evaluating AI’s societal impact before deployment. 👉Ignoring these assessments leaves your organization reacting to ethical failures instead of preventing them. 3. Integrate Ethics Throughout the AI Lifecycle ISO42001 embeds ethics at every stage of AI development: 🔸Design: Define fairness, security, and explainability objectives (Clause A.6.1.2). 🔸Development: Apply bias mitigation and explainability tools (Clause A.7.4). 🔸Deployment: Establish oversight, audit trails, and human intervention mechanisms (Clause A.9.2). 👉Ethical AI is not a last-minute check, it must be integrated/operationalized from the start. 4. Enforce AI Accountability & Human Oversight AI failures occur when accountability is unclear. ISO42001 requires: 🔸Defined responsibility for AI decisions (Clause A.9.2). 🔸Incident response plans for AI failures (Clause A.10.4). 🔸Audit trails to ensure AI transparency (Clause A.5.5). 👉Your governance must answer: Who monitors bias? Who approves AI decisions? Without clear accountability, ethical risks will become systemic failures. 5. Continuously Audit & Improve AI Ethics Governance AI risks evolve. Static governance models fail. ISO42001 mandates: 🔸Internal AI audits to evaluate compliance (Clause 9.2). 🔸Management reviews to refine governance practices (Clause 10.1). 👉AI ethics isn’t a magic bullet, but a continuous process of risk assessment, policy updates, and oversight. ➡️ AI Ethics Requires Real Governance AI ethics only works if it’s enforceable. Use ISO42001 to: ✅Turn ethical principles into actionable governance. ✅Proactively assess AI risks instead of reacting to failures. ✅Ensure AI decisions are explainable, accountable, and human-centered.

This. Adobe is knowingly facilitating the laundering of data and the subsequent sale of generative images relying on living, non-consenting artists/photographers/creators names as prompts. Adobe allows Stock contributors to upload these images to its Stock service, despite the fact that these images "go against their generative AI content policy," where the Stock contributors (the prompters, not the artists whose names are being co-opted without consent) then make money on their sale. Further, the images uploaded to Adobe Stock serve as the training inputs for Adobe Firefly, their so-called ethical poster child for generative AI image-making. They then put the burden of maintenance and policing on end-users. Marc Simonetti reported images using his name, and how often will he have to search Adobe Stock for his own name, for his handles, for fuzzy matches (non-exact spellings, deliberate typos, etc to bypass exact-match filters)? Especially when there is no consequence for the Stock contributors deliberately uploading MidJourney outputs violating Adobe's policy? How many artists don't have the time, the Adobe Stock subscription, the psychological energy to police an enterprise-grade platform because the enterprise behind it knows they can wait them out? Adobe has more than enough tools at its disposal to catch and prevent the uploading of generated images with questionable data provenance, or to prevent the uploading of images with non-consenting artists' names or styles (haveibeentrained already has a list of 1.4 billion image-based opt-outs by creators). So, Adobe, why aren't you? #ai #generativeai #genai #firefly #adobe #adobestock #datatheft #datalaundering #datadignity #consent #copyright #ethics

A teacher's use of AI to generate pictures of her students in the future to motivate them captures the potential of AI for good, showing students visually how they can achieve their dreams. This imaginative use of technology not only engages students but also sparks a conversation about self-potential and future possibilities. However, this innovative method also brings up significant ethical questions regarding the use of AI in handling personal data, particularly images. As wonderful as it is to see AI used creatively in education, it raises concerns about privacy, consent, and the potential misuse of AI-generated images. 𝐊𝐞𝐲 𝐈𝐬𝐬𝐮𝐞𝐬 𝐭𝐨 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫 >> Consent and Privacy: It's crucial that the individuals whose images are being used (or their guardians, in the case of minors) have given informed consent, understanding exactly how their images will be used and manipulated. >> Data Security: Ensuring that the data used by AI, especially sensitive personal data, is secured against unauthorized access and misuse is paramount. >> Ethical Use: There should be clear guidelines and purposes for which AI can use personal data, avoiding scenarios where AI-generated images could be used for purposes not originally intended or agreed upon. 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐢𝐨𝐧 >> Creators and Users of AI: Developers and users of AI technologies must adhere to ethical standards, ensuring that their creations respect privacy and are used responsibly. >> Legal Frameworks: Stronger legal frameworks may be necessary to govern the use of AI with personal data, specifying who is responsible and what actions can be taken if misuse occurs. As we continue to innovate and integrate AI into various aspects of life, including education, it's vital to balance the benefits with a strong commitment to ethical practices and respect for individual rights. 🤔 What are your thoughts on the use of AI to inspire students? How should we address the ethical considerations that come with such technology? #innovation #technology #future #management #startups

Responsible data development is at the core of Responsible AI (RAI). If a training dataset was created poorly (under-represented, skewed data) this will lead to a biased model. In AI development, using real data has privacy, ethical, and IP implications, to name a few. On the other hand, using synthetic (AI-generated) data is not a panacea (as much as it’s been hailed). It leads to other kinds of downstream issues that need to be taken into account. This paper explores two key risks of using synthetic data in AI model development: 1.      Diversity-washing (synthetic data can give the appearance of diversity) 2.      Consent circumvention (consent stops being a “procedural hook” that limits downstream harms from AI model use and this – along with data source obfuscation - complicates enforcement) The paper focuses on facial recognition technology (FRT) highlighting the risks of using synthetic data, and the trade-offs between utility, fidelity, and privacy. It’s important to develop participatory governance models along with data lineage and transparency which are crucial when it comes to mitigating these risks.