Fair Usage Guidelines for AI Services

As Prezent’s founder, I’ve seen first-hand how AI is changing the way we make decisions— It can make the process *much* faster and smarter. There is a lot of skepticism and mistrust around AI though… And rightfully so! Poorly built or managed AI can lead to ⤵ → Unfair treatment → Privacy concerns → No accountability (and more) So, here’s our approach toward ethical AI at Prezent: 1️⃣ Keeping data secure Your data's sacred. We're strict about protecting it, following laws like GDPR and CCPA. Privacy isn't a bonus — it's a baseline. 2️⃣ Putting fairness first Bias has no place here— We're on a mission to find and reduce biases in AI algorithms to make decisions fair for all… no picking favorites. 3️⃣ Being transparent AI shouldn't be a secret black box. We clearly explain how ours works and the decisions it makes. ↳ Openness → Trust among users 4️⃣ Monitoring often Keeping AI ethical isn't a one-and-done deal — it's an ongoing commitment. That said, We're always looking out for issues… Ready to adjust as necessary and make things better. 5️⃣ Engaging all stakeholders AI affects us all, so we bring *everyone* into the conversation. ↳ More voices + perspectives → Better, fairer AI 6️⃣ Helping humans We build AI to *help* people, not harm them— This means putting human values, well-being, and sustainability first in our actions and discussions. 7️⃣ Managing risk We're always on guard against anything that might go wrong… …from privacy breaches to biases. This keeps everyone safe. 8️⃣ Giving people data control Our systems make sure you're always in the driver's seat with your personal information. Your data, your control— Simple as that. 9️⃣ Ensuring data quality Great decisions *need* great data to back them up— So, our QA team works hard to ensure our AI is trained on diverse and accurate data. 🔟 Keeping data clean We’re serious about keeping our data clean and clear— Because well-labeled data → Better decisions In fact, it’s the *foundation* for developing trustworthy, unbiased AI. Truth is, getting AI ethics right is tough. But compromising our principles isn’t an option— The stakes are *too* high. Prezent’s goal? ↳ To lead in creating AI that respects human rights and serves the common good. Settling for less? Not in our DNA.

#GRC Today I led a session focused on rolling out a new Standard Operating Procedure (SOP) for the use of artificial intelligence tools, including generative AI, within our organization. AI tools offer powerful benefits (faster analysis, automation, improved communication) but without guidance, they can introduce major risks: • Data leakage • IP exposure • Regulatory violations • Inconsistent use across teams That’s why a well-crafted SOP isn’t just nice to have .. it’s a requirement for responsible AI governance. I walked the team through the objective: 1. To outline clear expectations and minimum requirements for engaging with AI tools in a way that protects company data, respects ethical standards, and aligns with core values. We highlighted the dual nature of AI (high value, high risk) and positioned the SOP as a safeguard, not a blocker. 2. Next, I made sure everyone understood who this applied to: • All employees • Contractors • Anyone using or integrating AI into business operations We talked through scenarios like writing reports, drafting code, automating tasks, or summarizing client info using AI. 3. We broke down risk into: • Operational Risk: Using AI tools that aren’t vendor-reviewed • Compliance Risk: Feeding regulated or confidential data into public tools • Reputational Risk: Inaccurate or biased outputs tied to brand use • Legal Risk: Violation of third-party data handling agreements 4. We outlined what “responsible use” looks like: • No uploading of confidential data into public-facing AI tools • Clear tagging of AI-generated content in internal deliverables • Vendor-approved tools only • Security reviews for integrations • Mandatory acknowledgment of the SOP 5. I closed the session with action items: • Review and digitally sign the SOP • Identify all current AI use cases on your team • Flag any tools or workflows that may require deeper evaluation Don’t assume everyone understands the risk just because they use the tools. Frame your SOP rollout as an enablement strategy, not a restriction. Show them how strong governance creates freedom to innovate .. safely. Want a copy of the AI Tool Risk Matrix or the Responsible Use Checklist? Drop a comment below.

The guide "AI Fairness in Practice" by The Alan Turing Institute from 2023 covers the concept of fairness in AI/ML contexts. The fairness paper is part of the AI Ethics and Governance in Practice Program (link: https://lnkd.in/gvYRma_R). The paper dives deep into various types of fairness: DATA FAIRNESS includes: - representativeness of data samples, - collaboration for fit-for-purpose and sufficient data quantity, - maintaining source integrity and measurement accuracy, - scrutinizing timeliness, and - relevance, appropriateness, and domain knowledge in data selection and utilization. APPLICATION FAIRNESS involves considering equity at various stages of AI project development, including examining real-world contexts, addressing equity issues in targeted groups, and recognizing how AI model outputs may shape decision outcomes. MODEL DESIGN AND DEVELOPMENT FAIRNESS involves ensuring fairness at all stages of the AI project workflow by - scrutinizing potential biases in outcome variables and proxies during problem formulation, - conducting fairness-aware design in preprocessing and feature engineering, - paying attention to interpretability and performance across demographic groups in model selection and training, - addressing fairness concerns in model testing and validation, - implementing procedural fairness for consistent application of rules and procedures. METRIC-BASED FAIRNESS utilizes mathematical mechanisms to ensure fair distribution of outcomes and error rates among demographic groups, including: - Demographic/Statistical Parity: Equal benefits among groups. - Equalized Odds: Equal error rates across groups. - True Positive Rate Parity: Equal accuracy between population subgroups. - Positive Predictive Value Parity: Equal precision rates across groups. - Individual Fairness: Similar treatment for similar individuals. - Counterfactual Fairness: Consistency in decisions. The paper further covers SYSTEM IMPLEMENTATION FAIRNESS, incl. Decision-Automation Bias (Overreliance and Overcompliance), Automation-Distrust Bias, contextual considerations for impacted individuals, and ECOSYSTEM FAIRNESS. -- Appendix A (p 75) lists Algorithmic Fairness Techniques throughout the AI/ML Lifecycle, e.g.: - Preprocessing and Feature Engineering: Balancing dataset distributions across groups. - Model Selection and Training: Penalizing information shared between attributes and predictions. - Model Testing and Validation: Enforcing matching false positive/negative rates. - System Implementation: Allowing accuracy-fairness trade-offs. - Post-Implementation Monitoring: Preventing model reliance on sensitive attributes. -- The paper also includes templates for Bias Self-Assessment, Bias Risk Management, and a Fairness Position Statement. -- Link to authors/paper: https://lnkd.in/gczppH29 #AI #Bias #AIfairness

The European Commission and the European Research Area Forum published "Living guidelines on the responsible use of generative artificial intelligence in research." These guidelines aim to support the responsible integration of #generative #artificialintelligence in research that is consistent across countries and research organizations. The principles behind these guidelines are: • Reliability in ensuring the quality of research and awareness of societal effects (#bias, diversity, non-discrimination, fairness and prevention of harm). • Honesty in developing, carrying out, reviewing, reporting and communicating on research transparently, fairly, thoroughly, and impartially. • Respect for #privacy, confidentiality and #IP rights as well as respect for colleagues, research participants, research subjects, society, ecosystems, cultural heritage, and the environment. • Accountability for the research from idea to publication, for its management, training, supervision and mentoring, underpinned by the notion of human agency and oversight. Key recommendations include: For Researchers • Follow key principles of research integrity, use #GenAI transparently and remain ultimately responsible for scientific output. • Use GenAI preserving privacy, confidentiality, and intellectual property rights on both, inputs and outputs. • Maintain a critical approach to using GenAI and continuously learn how to use it #responsibly to gain and maintain #AI literacy. • Refrain from using GenAI tools in sensitive activities. For Research Organizations • Guide the responsible use of GenAI and actively monitor how they develop and use tools. • Integrate and apply these guidelines, adapting or expanding them when needed. • Deploy their own GenAI tools to ensure #dataprotection and confidentiality. For Funding Organizations • Support the responsible use of GenAI in research. • Use GenAI transparently, ensuring confidentiality and fairness. • Facilitate the transparent use of GenAI by applicants. https://lnkd.in/eyCBhJYF

👏 AI friends - a great model AI use policy came from an unlikely place: my physical mailbox! See photo and text below. Principles include informed consent, transparency, accountability, and training. Importantly -- the regulator here explains that AI is "here to stay" and an important tool in serving others. Kudos to Santa Cruz County Supervisor Zach Friend for this well-written, clear, non-scary constituent communication on how the county is working with AI. Also tagging my friend Chris Kraft, who writes on AI in the public sector. #AI #LegalAI • Data Privacy and Security: Comply with all data privacy and security standards to protect Personally Identifiable Information (PIl), Protected Health Information (PHI), or any sensitive data in generative Al prompts. • Informed Consent: Members of the public should be informed when they are interacting with an Al tool and have an "opt out" alternative to using Al tools available. • Responsible Use: Al tools and systems shall only be used in an ethical manner. • Continuous Learning: When County provided Al training becomes available, employees should participate to ensure appropriate use of Al, data handling, and adherence to County policies on a continuing basis. • Avoiding Bias: Al tools can create biased outputs. When using Al tools, develop Al usage practices that minimize bias and regularly review outputs to ensure fairness and accuracy, as you do for all content. • Decision Making: Do not use Al tools to make impactful decisions. Be conscientious about how Al tools are used to inform decision-making processes. • Accuracy: Al tools can generate inaccurate and false information. Take time to review and verify Al-generated content to ensure quality, accuracy, and compliance with County guidelines and policies. • Transparency: The use of Al systems should be explainable to those who use and are affected by their use. • Accountability: Employees are solely responsible for ensuring the quality, accuracy, and regulatory compliance of all Al-generated content utilized in the scope of employment.

State Bar of California approves guidance on use of generative AI in the practice of law. Key points: 🔹 A lawyer must not input any confidential information of the client into any generative AI solution that lacks adequate confidentiality and security protections. A lawyer must anonymize client information and avoid entering details that can be used to identify the client. (Duty of confidentiality) 🔹 AI-generated outputs can be used as a starting point but must be carefully scrutinized. They should be critically analyzed for accuracy and bias, supplemented, and improved, if necessary. (Duty of competence and diligence) 🔹 A lawyer must comply with the law (e.g. IP, privacy, cybersecurity) and cannot counsel a client to engage, or assist a client in conduct that the lawyer knows is a violation of any law, rule, or ruling of a tribunal when using generative AI tools. (Duty to comply with the law) 🔹 Managerial and supervisory lawyers should establish clear policies regarding the permissible uses of generative AI and make reasonable efforts to ensure that the firm adopts measures that give reasonable assurance that the firm’s lawyers and non lawyers’ conduct complies with their professional obligations when using generative AI. This includes providing training on the ethical and practical aspects, and pitfalls, of any generative AI use. (Duty to Supervise) 🔹 The lawyer should consider disclosure to their client that they intend to use generative AI in the representation, including how the technology will be used, and the benefits and risks of such use. A lawyer should review any applicable client instructions or guidelines that may restrict or limit the use of generative AI (Duty to communicate) 🔹 A lawyer may use generative AI to more efficiently create work product and may charge for actual time spent (e.g., crafting or refining generative AI inputs and prompts, or reviewing and editing generative AI outputs). A lawyer must not charge hourly fees for the time saved by using generative AI. (Charging for work produced by AI) 🔹 A lawyer must review all generative AI outputs, including, but not limited to, analysis and citations to authority for accuracy before submission to the court, and correct any errors or misleading statements made to the court. (Duty of candor to tribunal) 🔹 Some generative AI is trained on biased information, and a lawyer should be aware of possible biases and the risks they may create when using generative AI (e.g., to screen potential clients or employees). (Prohibition on discrimination) 🔹 A lawyer should analyze the relevant laws and regulations of each jurisdiction in which a lawyer is licensed to ensure compliance with such rules. (Duties in other jurisdictions) #dataprivacy #dataprotection #AIprivacy #AIgovernance #privacyFOMO Image by vectorjuice on Freepik https://lnkd.in/dDUuFfes

In light of the recent discussions around the European Union's Artificial Intelligence Act (EUAI Act), it's critical for brands, especially those in the fashion industry, to understand the implications of AI usage in marketing and beyond. The EU AI Act categorizes AI risks into four levels: unacceptable, high, limited, and minimal risks. For brands employing AI for marketing content, this predominantly falls under limited risks. While not as critical as high or unacceptable risks, limited risks still necessitate a conscientious approach. Here’s what brands need to consider: Transparency: As the backbone of customer trust, transparency in AI-generated content is non-negotiable. Brands must clearly label AI-generated services or content to maintain an open dialogue with consumers. Understanding AI Tools: It's not enough to use AI tools; brands must deeply understand their mechanisms, limitations, and potential biases to ensure ethical use and compliance with the EUAI Act. Documentation and Frameworks: Implementing thorough documentation of AI workflows and frameworks is essential for demonstrating compliance and guiding internal teams on best practices. Actionable Tips for Compliance: Label AI-Generated Content: Ensure any AI-generated marketing material is clearly marked, helping customers distinguish between human and AI-created content. Educate Your Team: Conduct regular training sessions for your team on the ethical use of AI tools, focusing on understanding AI systems to avoid unintentional risks. Document Everything: Maintain detailed records of AI usage, decision-making processes, and the tools' roles in content creation. This will not only aid in compliance but also in refining your AI strategy. Engage in Dialogue with Consumers: Foster an environment where consumers can express their views on AI-generated content, using feedback to guide future practices. For brands keen on adopting AI responsibly in their marketing, it's important to focus on transparency and consumer trust. Ensure AI-generated content is clearly labeled, allowing consumers to distinguish between human and AI contributions. Invest in understanding AI's capabilities and limitations, ensuring content aligns with brand values and ethics. Regular training for your team on ethical AI use and clear documentation of AI's role in content creation processes are essential. These steps not only comply with regulations like the EU AI Act but also enhance brand integrity and consumer confidence. To learn more about more about EU AI act impact on brands check out https://lnkd.in/gTypRvmu

On October 11, 2023, the French Data Protection Authority (the “CNIL”) published a new set of guidelines addressing the research and development of AI systems from a data protection perspective (the “Guidelines”). In the Guidelines, the CNIL confirms the compatibility of the EU General Data Protection Regulation (“GDPR”) with AI research and development. The Guidelines are divided into seven “AI how-to sheets”, these guides: (1) determining the applicable legal regime (e.g., the GDPR or the Law Enforcement Directive); (2) adequately defining the purpose of processing; (3) defining the role (e.g., controller, processor, or joint controller) of AI system providers; (4) defining the legal basis and implementing necessary safeguards to ensure the lawfulness of the data processing; (5) drafting a data protection impact assessment (“DPIA”) where necessary; (6) adequately considering data protection in the AI system design choices; and (7) implementing the principle of data protection by design in the collection of data and adequately managing data after collection. Noteworthy takeaways from the Guidelines include: In line with the GDPR, the purpose of the development of an AI system must be specific, explicit, and legitimate. The CNIL clarifies that where the operational use of AI systems in the deployment phase is unique and precisely identified from the development stage, the processing operations carried out in both phases pursue, in principle, a single overall purpose. Consent, legitimate interests, contract performance, and public interest may all theoretically serve as legal bases for the development of AI systems. Controllers must carefully assess the most adequate legal basis for their specific case. DPIAs carried out to address the processing of data for the development of AI systems must address specific AI risks, such as the risk of producing false content about a real person or the risks associated with known attacks specific to AI systems (such as attacks by data poisoning, insertion of a backdoor, or model inversion). Data minimization and data protection measures that have been implemented during data collection may become obsolete over time and must be continuously monitored and updated when required. Re-using datasets, particularly those publicly available on the Internet, is possible to train AI systems, provided that the data was lawfully collected and the purpose of re-use is compatible with the original collection purpose. The CNIL considers AI to be a topic of priority. It has set up a dedicated AI department, launched an action plan to clarify the rules and support innovation in this field, and introduced two support programs for French AI players. What do you think about the CNIL's Guidelines on AI development and data protection? #France #DPA #dataprotection #ai

The UK’s Medicines and Healthcare products Regulatory Agency (MHRA) sets out principles for Artificial Intelligence ahead of planned UK regulation: 🤖The MHRA has published a white paper outlining the need for specific regulation of AI in healthcare, emphasizing the importance of making AI-enabled health technology not only safe but also universally accessible 🤖 The agency is advocating for robust cybersecurity measures in AI medical devices and plans to release further guidance on this issue by 2025 🤖 It stresses the importance of international alignment in AI regulation to avoid the UK being at a competitive disadvantage and calls for upgraded classifications for certain AI devices that currently do not require authorization before market entry. 🤖MHRA has implemented the five key principles of AI usage: safety, security, transparency, fairness, and accountability. These principles aim to ensure AI systems are robust, transparent, fair, and governed by clear accountability mechanisms. 🤖The MHRA particularly emphasize transparency and explainability in AI systems, requiring companies to clearly define the intended use of their AI devices and ensure that they operate within these parameters 🤖Fairness is also highlighted as a key principle, with a call for AI healthcare technologies, to be accessible to all users, regardless of their economic or social status. 🤖The MHRA recently introduced the "AI Airlock", a regulatory sandbox that allows for the testing and refinement of AI in healthcare, ensuring AI's integration is both safe and effective 👇Link to article and white paper in comments #digitalhealth #AI

Bias in AI = Ad fairness? Understanding AI bias is crucial for ethical advertising. AI can perpetuate biases from training data, impacting ad fairness. I've written an article for Forbes Technology Council "Understanding And Mitigating AI Bias In Advertising" (link in comments), synopsis: Key Strategies: (a) Transparent Data Use: Ensure clear data practices. (b) Diverse Datasets: Represent all demographic groups. (c) Regular Audits: Conduct independent audits to detect bias. (d) Bias Mitigation Algorithms: Use algorithms to ensure fairness. Frameworks & Guidelines: (a) Fairness-Aware Tools: Incorporate fairness constraints  (TensorFlow Fairness Indicators from Google and IBM’s AI Fairness 360) (b) Ethical AI Guidelines: Establish governance and transparency. (c) Consumer Feedback Systems: Adjust strategies in real-time. Follow Evgeny Popov for updates. #ai #advertising #ethicalai #bias #adtech #innovation