Strategies for Equitable GenAI Integration

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

In Nov 2021, a huge wave of Gen AI hit the market with the launch of ChatGPT. However, there is something significant that often gets ignored: As Gen AI became the talk of the town, businesses began to adapt it for growth. At Quadrant Technologies, we have worked on a myriad of Gen AI projects with some incredible organizations. But soon, we realized its dark side that not many talk about : 👉 Threats of Generative AI Technology reflects society. The threats of GenAI include biases, influence, lack of transparency, hallucination, ethics, and much more. These threats can impact people’s decisions, experiences, and lives. 👉 The Solution: RESPONSIBLE AI As it has been said, with great power comes great responsibility. To reduce the effects of all these threats, Responsible AI comes into the picture. It is more than a buzzword. It ensures that AI will be used for the greater good of humanity and not as a threat. Many ways have now emerged to ensure responsible AI. One of these is the OECD AI Principles, offered by the Organization for Economic Co-operation and Development. At Quadrant Technologies, we helped organizations use this framework to mitigate the risks of GenAI. Here is that 6-component framework: 1/ Fairness: AI systems should treat all individuals equally. For this, businesses should recognize potential biases and work towards preventing them. 2/ Transparency: AI-powered apps have the power to influence our decisions. Therefore, companies should be transparent about how the AI models are trained. 3/ Inclusiveness: AI technology should address the needs of diverse individuals and groups. Organizations must ensure that their AI systems follow inclusivity. 4/ Accountability: Organizations must take responsibility for any negative impacts caused by their AI systems, proactively identifying and mitigating risks. 5/ Reliability & Safety: AI systems should be built and tested to ensure they operate safely and effectively, minimizing harm and accidents through thorough testing and risk assessment. 6/ Privacy & Security: AI models should be designed to respect users' privacy and secure their data. This means preventing models from improperly accessing or misusing personal information, ensuring data protection from the AI's perspective. Here are the ways tech organizations can embed this framework into their culture: 📍 Train and educate: Teach teams about ethical AI principles and bias risks. 📍Detect AI bias before scaling: Test for biases at every stage of scaling. 📍Community management: Engage with affected communities for feedback to ensure fairness and inclusivity. ------------ AI is here to stay. Ensuring that we develop and use it responsibly is the only way to leverage it for the betterment of society. What's your perspective? #genai #aisytems #threat

Ensuring Equity and Inclusion in AI Adoption: Insights from My Podcast with Robert Lawrence Wilson I had the pleasure of discussing the critical topic of Bias in AI with Robert on his recent podcast. We discussed the history of AI and its growing impact on modern businesses. Our conversation took a deep dive into the various types of biases that can emerge in AI systems, from data and algorithmic bias to the human biases that shape AI development and deployment. One key takeaway from our discussion: as organizations increasingly adopt AI, it is crucial to incorporate diversity, equity, and inclusion considerations into every stage of the process. This means ensuring that the data used to train AI models is representative and inclusive, reflecting the diversity of the populations the AI will serve. It also means designing algorithms that prioritize fairness and equity, and subjecting them to rigorous bias testing and auditing. Critically, DEI must be at the forefront of how AI is applied across various business functions. ➡ In recruiting, AI tools should be used to enhance diversity and mitigate bias in hiring decisions. ➡ For career advancement, AI systems must be designed to provide equitable opportunities and counter historical disparities. ➡ Corporate policies and communications shaped by AI should undergo careful review to ensure they are inclusive and free from bias. Ultimately, the successful integration of AI in business requires a proactive, informed approach. It demands collaboration among AI developers, business leaders, HR professionals, and people and culture experts. Only by working together can we harness the power of AI to drive innovation and efficiency while also promoting equity and inclusion. The podcast will be released in May/June and I will share when it is ready. Let's continue this crucial conversation and work towards a future where AI serves as a tool for greater fairness and representation in the workplace. #AI #Bias #Diversity #Equity #Inclusion #DEI #Recruiting #CareerAdvancement #CorporatePolicy #HumanResources

Generative AI systems are increasingly evaluated for their social impact, but there's no standardized approach yet. This paper from June 2023 presents a framework for evaluating the social impact of generative AI systems, catering to researchers and developers, third-party auditors and red-teamers, and policymakers. Social impact is defined by the authors "as the effect of a system on people and communities along any timeline with a focus on marginalization, and active, harm that can be evaluated." The framework defines 7 categories of social impact: - bias, stereotypes, and representational harms; - cultural values and sensitive content; - disparate performance; - privacy and data protection; - financial costs; - environmental costs; - data and content moderation labor costs. E.g., the paper explains that safeguarding personal information and privacy relies on proper handling of training data, methods, and security measures. The paper stresses that there is great potential for more comprehensive privacy evaluations of GenAI systems: - Addressing the issue of memorization of training examples. - Ensure that only lawfully obtained data is shared with third parties. - Prioritize individual consent and choices. GenAI systems are harder to evaluate without clear documentation, systems for obtaining consent (e.g., opt-out mechanisms), and appropriate technical and process controls. Rules for leveraging end-user data for training purposes are often unclear, and the immense size of training datasets makes scrutiny increasingly difficult. Therefore, privacy risk assessments should go beyond proxies, focusing on memorization, data sharing, and security controls, and require extensive audits of processes and governance. 5 overarching categories for evaluation in society are suggested: - trustworthiness and autonomy; - inequality, marginalization, and violence; - concentration of authority; - labor and creativity; - ecosystem and environment. Each category includes subcategories and recommendations for mitigating harm. E.g., the category of trustworthiness and autonomy includes "Personal Privacy and Sense of Self". The authors emphasize that the impacts and harms from the violation of privacy are difficult to enumerate and evaluate. Mitigation first should determine who is responsible for an individual’s privacy, but requires both individual and collective action. The paper points out that technical methods to preserve privacy in a GenAI system, as seen in privacy-preserving approaches to language modeling, cannot guarantee full protection. Improving common practices and better global regulation for collecting training data can help. By Irene Solaiman, Zeerak Talat, William Agnew, Lama Ahmad, Dylan Baker, Su Lin Blodgett, Hal Daumé III, Jesse Dodge, Ellie Evans, Sara Hooker, Yacine Jernite, Alexandra Sasha Luccioni, Alberto Lusoli, Margaret Mitchell, Jessica Newman, Marie-Therese Png, Andrew Strait, Apostol Vassilev

🌐 Unveiling the Dual Impact of Generative AI on Black Communities: Insights from McKinsey & Company's Latest Report 🔍 McKinsey & Company's recent report sheds light on the profound impact of generative AI on Black communities in the U.S., revealing a complex landscape of both challenges and opportunities. What we learned: 📈 The Racial Wealth Gap Intensifies: Generative AI is set to create a wealth boom, but not for everyone equally. The racial wealth gap could widen by a staggering $43 billion annually, with Black Americans capturing significantly less of the new wealth generated. 🤖 Automation's Threat to Jobs: Black workers face a disproportionate risk in the age of AI, being overrepresented in jobs most susceptible to automation. This trend raises urgent questions about job security and the need for reskilling. 🚀 The Shift to Future-Proof Skills: The focus is shifting from future-proof jobs to skills. Socioemotional abilities, physical presence, and nuanced problem-solving are key skills less likely to be automated, offering a buffer against AI-induced job displacement. 🌟 Opportunities in Healthcare & Finance: There's a silver lining: gen AI could revolutionize healthcare access for Black Americans and enhance financial inclusion, breaking down longstanding barriers. 🔎 Equitable AI Deployment is Crucial: The report emphasizes the need for equitable AI deployment. This includes reskilling initiatives, careful application of AI, and robust regulatory frameworks to ensure AI benefits are shared across all communities. Renée Cummings Dr. Aaron Smith Dr. Jackie “. Elizabeth Leiba Samantha Katz Mike Green #AI #RacialWealthGap #TechnologyImpact #InclusiveInnovation