Riley Coleman’s Post

🔍 AI audits are becoming critical for Responsible AI development, ensuring ethical practices, reducing bias, and building stakeholder trust in AI systems. 🌟 With regulations like the EU AI Act emphasizing audits, organizations are now prioritizing transparency and accountability in AI systems. 🚀 ✨ Just completed the "Introduction to Auditing AI Systems" certification! ✨ Key Learnings- 1️⃣ 📜 Understanding of the 4 components of AI audits: public disclosure, internal procedures, code tests, and documentation. 2️⃣ 🗂️ Insights into the 3 types of AI audits: Data audit, Model audit, and System/Continuous audit. 3️⃣ 🔎 AI audit results: Identify improvement areas, develop harm mitigation plans, ensure compliance, and build stakeholder trust. 4️⃣ ⚠️ Recognizing 3 types of AI system harms: Allocation harms, Quality of Service harms, and Stereotyping harms. 5️⃣ 💻 AI audits expose improvement areas in data quality, data collection practices, model choice, and deployment environments. 6️⃣ 🇪🇺 EU AI Act outlines the need for pre-use AI audits and compliance with GDPR for training data. 7️⃣ 📊 AI audits measure bias, disparity of outcomes, and compliance. 8️⃣ 🔬 Performance metrics: Accuracy, Precision, Recall, and factors like data quality, features, and compute. 9️⃣ ⚖️ Metrics to measure fairness: Equal opportunity, Equalizer odds, Demographic parity, and Classification error rate. 🔟 ⚙️ Bias mitigation: A critical step before model deployment. 1️⃣1️⃣ 🎯 Data sampling methods: Convenience, Random, Stratified, and Cluster sampling. 1️⃣2️⃣ 🧠 Types of bias: Sampling, Selection, Confirmation, and Labeling bias. 1️⃣3️⃣ 🌍 Human and cultural biases: Outcomes of labeling bias. 1️⃣4️⃣ 🤖 Principles of Explainable AI: Explainability, Meaningfulness, Explanation accuracy, and Knowledge limits. 1️⃣5️⃣ 🔍 Transparency levels: Basic, Functional, Algorithmic, and Full transparency. 1️⃣6️⃣ 💡 5 Responsible AI principles: Transparency, Explainability, Reliability, Fairness, Sustainability, and Compliance. 1️⃣7️⃣ 🔄 7 AI Bias Audit stages: Scoping, Mapping, Artifact collection, Testing, Reflection, Recommendations, and Reporting. 1️⃣8️⃣ 🔂 Continuous AI audits: Enable proactive corrective actions to prevent unmitigated harms. 💡 Why this matters? AI audits are not just a regulatory requirement—they are a trust-building mechanism that fosters ethical, transparent, and impactful AI systems. 🌟 #ArtificialIntelligence #AI #AIAudits #ResponsibleAI #EthicalAI #AICompliance #AILeadership #AIForBusiness #TechnologyLeadership #HR #BusinessExcellence #DataScience #TechInnovation #MachineLearning #AIRegulations #Transparency #Fairness #Compliance #AITrust

As #AI ramps up in usage, so do fears surrounding its safety, how it is being trained and where that information is coming from. After all, the information that AI models come up with when you type in a question or ask it to do something comes from somewhere. With #SyntheticData gaining popularity, MARKETING-INTERACTIVE spoke to two AI experts including Siddharth Jhanji and Milind . to find out how it could be the answer to privacy and accuracy concerns when it comes to the use of AI platforms. #tech

Most of the things you can read on how to explain and frame AI is focused on #tech. 💻 vs👩💻 Yes, we believe it’s crucial to understand how it’s made (if only to have a rational chat about ethics and where it’s at): How AIs are trained; How #deeplearning has completely changed the game; How far are we technically from replicating human intelligence? Are all fascinating questions that will help us shape our minds to the possibilities of AI. But we believe it’s useful to frame #AI in a way beyond technicalities and look at it only from the usage perspective. In the discussions we’ve had, we find too often that tools built with the same tech (e.g. LLMs) are put in the same bag, when they can offer completely different usages based on how we use them. The following framework tries to capture AI only through #usage. From this perspective you can put aside the AI tech and focus on what these AI can accomplish for you. We’re glad to get opinions from you to improve it. 1st, a matrix : The X axis gravitates around the output. From pure content to concrete action. This captures gradually what AI can put on the table. The Y axis captures how it will generate the output : Regurgitating existing data in a structured way (we called extrapolate), or actually creating something entirely new (as non-existing before). This outlines 7 major uses for AI : Informative AI (extrapolating / content) : retrieves existing and available information you need to answer a question, and syndicates it in the most efficient way for you to use. Predictive AI (extrapolating / guidance) : This category uses available data (often not easily interpretable) to turn it into information that we can use for action. Decision AI (extrapolating / action) : This category uses available data and rules to push buttons and levers and create a course of action. It's the world of automation. Creative AI (creating / content) : this is quite the trend at the moment, where the AI tool can create a content (from video to poems) based on prompts. Companion AI (creating / guidance) : explores the huge opportunity of recreating the benefits of relationships. Assistant AI (creating / decisions) : can create action based on its own perception of the environment. So what matters is the live interpretation and assessment of that data, whichever form it takes. It’s mimicking the human brain. We’re very far from it. Conversational AI (a bit of everything) : In the middle of the framework, there’s a tool, that is not yet an assistant, but not just an aggregator of information neither. It can uphold a quite natural discussion, but will avoid deep discussions. It can predict a few things, but is limited to its knowledge. It can create stuff, but modestly. We can see it as a primer and a router of all other tasks. That’s that. Feel free to reach out in the comment section to share your views on this matter. How does this framework fit into your approach to AI-led productivity?

As #AI ramps up in usage, so do fears surrounding its safety, how it is being trained and where that information is coming from. After all, the information that AI models come up with when you type in a question or ask it to do something comes from somewhere. With synthetic data gaining popularity, MARKETING-INTERACTIVE spoke to two AI experts including Siddharth Jhanji and Milind . to find out how it could be the answer to privacy and accuracy concerns when it comes to the use of AI platforms. #tech #SyntheticData

I had the opportunity to reflect on the role of human-centered AI thanks to a survey I participated in, Dr. Aung Pyae I appreciate the invitation! I consider that AI has the potential to transform industries and improve lives, but the real challenge lies in ensuring it aligns with human values and needs. Bias in data, lack of transparency, and the delicate balance between privacy and personalization remain significant barriers to truly human-centered AI systems. In my point of view, overcoming these challenges will require diverse, representative data, explainable models, and ethical frameworks that prioritize fairness, trust, and human autonomy. Collaboration, collaboration, and more collaboration among educators, technologists, ethicists, and policymakers! A human-centered AI system should be designed with the primary goal of enhancing human well-being, respecting human values, and being aligned with human needs and intentions. Here's what I believe makes an AI system human-centered: 1. User-Centric Design: The AI should prioritize the user's needs and experiences, ensuring that its interface and functions are intuitive, accessible, and easy to use for a broad range of individuals, regardless of their technical skills or background. 2. Ethical and Transparent Operation: A human-centered AI operates transparently, meaning its decision-making processes are explainable and interpretable to users. It avoids opaque "black-box" algorithms where users have no understanding or control over outcomes. Ethical considerations, like privacy, fairness, and avoidance of bias, should be central. 3. Supportive of Human Autonomy: Rather than replacing human judgment or action, a human-centered AI augments human decision-making. It empowers users by providing insights, guidance, or support, while allowing them to remain in control of the final decisions. 4. Emphasis on Collaboration: Human-centered AI systems are designed to work in partnership with humans, enabling collaboration where AI complements human strengths, such as creativity, empathy, or critical thinking. The goal is not to replace humans but to enhance their abilities. 5. Adaptability and Personalization: The AI should be able to adapt to individual preferences, learning from interactions to provide personalized experiences. This includes adjusting to various cultural, emotional, and contextual factors in a user's environment. 6. Trust and Reliability: Human-centered AI builds trust through consistent performance, safety features, and mechanisms that allow users to verify and understand the accuracy of the AI's actions. What do you think are the biggest hurdles to achieving human-centered AI? #HI4AI #human #HumanCenteredAI #AIethics #AIFuture #TrustworthyAI #AIInnovation #BiasInAI #AITransparency #AIForAll #education #studentfocus #AIliteracy #AIpolicies #education #highereducation

Companies face challenges integrating generative AI into daily operations due to concerns about accuracy, bias, and data privacy. Solution: Organizations are increasingly adopting AI for tasks like document editing and data analysis, with usage rising from 37% in 2023 to 72% in 2024. This shift enhances efficiency across various business functions. Learn more! https://buff.ly/4fXccws #AIAdoption #BusinessEfficiency #GenerativeAI

How To Develop An Effective AI Policy

#AI has a key role to play in building high-performing, resilient and responsive enterprises in an increasingly connected world. However, the rapid rise in content in recent years has led to a surge of data, which #GenAI could help us to navigate and gain insights. To harness GenAI effectively, organizations can follow three strategies to gain insights without exposure to the harmful consequences of unchecked AI use: 1. Use AI for enhancing total information awareness, 2. Curate a collection of content that you can rely on, 3. Grow usage while maintaining control over your technology. https://lnkd.in/gcV4a3Ni #worlddatasummit #datainnovation #dataarchitecture #dataanalytics #datascience #dataquality #dataprotection #dataethics #datasecurity

Once AI models use your data, removing it isn’t as easy as hitting “delete.” Your data becomes deeply embedded in the AI’s learned behavior, which makes “forgetting” it a complex challenge. In my latest article, I explore why it’s so difficult to delete data from AI models, how this impacts user consent and privacy, and the emerging concept of machine unlearning as a potential solution. I also discuss LinkedIn’s recent GenAI opt-in controversy and provide tips on how users can opt out 😉 https://lnkd.in/gFD6y6-x #AI #DataPrivacy #MachineLearning #EthicalAI #UserConsent #Tech

#ThrowbackThursday: AI in 2024 - Focus on Data, Governance, and Upskilling Remember the debates around data accuracy and AI regulation? A year ago, we were just starting to grapple with these critical issues. How did your company approach responsible AI development in 2024? #TBT #AI #FutureofWork