CHARLIE Project’s Post

#Artificial #intelligence and machine learning based solutions hold a promise of becoming a transformative force in multiple facets of humanitarian and development work. However, alongside this lies the potential of negative implications in the form of furthering entrenched biases and exacerbating inequality. The the Gender Equitable AI Toolkit from NetHope presents principles developed to provide a framework for ensuring gender equitable AI. NetHope identified 5 sets of principles for gender equitable AI development in the following areas: Fairness and inclusivity. Transparency. Design and development. Governance and autonomy. Collaboration and capacity building. To mitigate risk, it is critical that implementing organizations adopt principles – like the above – and thereby champion gender equity. 1. #Accessibility AI initiatives must tackle challenges related to equitable access to digital resources. This involves bridging the gap for individuals across the globe, especially those in underserved regions. The overarching goal is to protect the rights of individuals and prevent harm in the design and deployment of AI solutions. 2. #Gender-Centricity By establishing comprehensive metrics and objectives that effectively advance both facets, organizations can systematically measure the impact of AI on programmatic effectiveness and gender equity, thereby fostering fairness in the conception and execution of AI technologies. 3. #Bias Mitigation Design and development principles guide the creation of AI systems that are accessible, unbiased, and responsive to the needs and aspirations of diverse individuals and communities. This approach fosters the development of AI systems that are impactful and capable of addressing societal challenges while leaving no one behind. 1. #Gender Centered Design AI initiatives must center solutions around the needs, perspectives, and leadership of priority gender groups at the local level. 2. #Intersectionality AI initiatives should recognize that gender is a critical aspect of an individual’s identity, and that like any human group, deserves dignity and visibility in digital ecosystems. This principle emphasizes moving beyond a monolithic understanding of gender and pushes practitioners to consider gender identity during data collection and curation. 3. #Model Awareness AI initiatives should emphasize the importance of training models in ways that raise awareness of fairness, inclusivity, and equal representation of diverse gender groups. https://lnkd.in/dvvjTxNx

🔍✨ 𝗔𝗜 𝗶𝗻 𝘁𝗵𝗲 𝟮𝟭𝘀𝘁 𝗖𝗲𝗻𝘁𝘂𝗿𝘆: 𝗕𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗦𝘁𝗲𝗿𝗲𝗼𝘁𝘆𝗽𝗲𝘀, 𝗼𝗿 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗶𝗻𝗴 𝗧𝗵𝗲𝗺? ✨🔍 A new UNESCO study reveals an unexpected twist in the rise of GenerativeAI: the risk of regressive gender stereotypes! Rather than pushing boundaries, many AI models are mirroring outdated biases that don’t align with today’s push for equality. From stereotyped career roles (think “nurse” 👩⚕️ vs. “engineer” 👨🔧) to assumptions about personality traits, AI appears to be drawing from outdated frameworks and assumptions. 🌱 𝗦𝗼, 𝘄𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆? We have the power to shape AI responsibly. By training these models with diverse, unbiased data, we can guide AI towards becoming a true tool of the future—reflecting all of us fairly. The article of UNESCO proposes several measures to address gender bias in AI, including: 🔸 Developing AI models with more diverse and representative datasets. 🔸 Implementing transparency in AI algorithms to detect and mitigate bias. 🔸 Encouraging collaboration between AI developers, policymakers, and civil society to create ethical guidelines. 🔸 Promoting educational initiatives to raise awareness about gender bias in AI systems. You can read more here: https://lnkd.in/gWQC7ZcK #breakingstereotypes #gendergap #womeninAI #genderbias #futureofAI #empoweringwomen #inclusivity #STREAMIT #worldscienceday

"AI technologies are largely developed in the Global North–without necessarily considering differences across and within developing countries—and so, the international development community has an important role to play to support and advance approaches for more equitable AI.... Digital gender data gaps stem from differences in Internet and smartphone access and use, which are larger in many Global South countries, and these data gaps impact what and from whom machine learning tools learn." As a global southerner, I have experience firsthand how these product design decisions historically exclude people of colour, women, and other marginalized communities. This is why Genevieve Smith's article "How to Make AI Equitable in the Global South" is so relevant. #responsibleAI #equitableAI #genderdigitaldivide Link: https://lnkd.in/eXuhaBSC

🌟Honoured to be recognised as one of the Top Women in AI in 2024!!🌟 In DailyAI’s own words, “Despite persistent gender disparities in the tech industry, you stand out as a trailblazer, playing a pivotal role in shaping the future of AI innovation and technology”. Thanks for the recognition but most importantly thanks to every woman working hard to make AI technologies safe for everyone! (And everyone else working on this!) https://lnkd.in/emz-gfnT #ai #responsibleai

Josephine Lethbridge, this is a very interesting article where you bring to the fore aspects of AI and how sexist it can be. I am really happy to have contributed my thoughts to the topic. Its a good read: https://lnkd.in/gqBvESp5

Generative AI: UNESCO study reveals alarming evidence of regressive gender stereotypes. Ahead of the International Women's Day, a UNESCO study revealed worrying tendencies in Large Language models (LLM) to produce gender bias, as well as homophobia and racial stereotyping. Women were described as working in domestic roles far more often than men ¬– four times as often by one model – and were frequently associated with words like “home”, “family” and “children”, while male names were linked to “business”, “executive”, “salary”, and “career”. #LLMs #tech #bias #generativeAi

__Daily AI Posts__ May 24, 2024 AI Roundup: Artificial Intelligence and Gender Equality; Google's AI Summaries Cause ... - InfoDocket AI Roundup: Artificial Intelligence and Gender Equality; Google's AI Summaries Cause Headaches and Spawn Memes; Generative AI Search Engines as ... https://lnkd.in/gpH3eM29 Youdao's Multiple Financial Indicators Exceed Market Expectation - WICZ Currently, Youdao has launched more than 10 native applications based on its LLM . In the first quarter, Youdao's AI-driven subscription services saw a ... https://lnkd.in/gf-Xgf7m McKesson, Merck back Atropos Health's $33M round to accelerate drug development with AI Credit: VentureBeat made with Midjourney · 'Legitimately dangerous': Google's erroneous AI Overviews spark mockery, concern · Cracked gold rimmed ... https://lnkd.in/gt-3NNvT OpenAI's Sam Altman Wrote The First Check Into This $300 Million Startup. Now It's ... - Forbes OpenAI's Sam Altman Wrote The First Check Into This $300 Million Startup. Now It's Creating Drones For 911 Emergencies ... Click to save this article. https://lnkd.in/gJjxRDfd Happy reading! #ai #ml #ainews #llm #genai

AI is having difficulty generating images of men doing domestic labour. This is due to how it has been trained to see the world, through the 20 million+ entries in the dataset it has learnt from. Our content. How will this 'bias' impact AI's ability to assess candidates for leadership roles? ANZ is already using AI technology to assess home-loan applications. What are the implications for women and others?

🚽 All I wanted was an AI image of a man cleaning a toilet 🚽 Possibly one of the best titles to an article I have ever seen! Courtesy of The Female Lead, Dr. Samantha Pillay explores the gender biases embedded in AI-generated images, particularly in relation to domestic tasks. Some key takeaways: 1. Gender Bias in AI: in attempting to generate an image of a man cleaning a toilet she found that AI struggled to create a plausible or competent depiction of men doing domestic chores, reflecting common stereotypes that associate women with these tasks. In contrast, generating an image of a woman cleaning a toilet was significantly easier, with the results appearing more natural and competent. 2. AI Training and Stereotypes: AI models, such as Midjourney, are trained on vast datasets pulled from the internet, which often contain gendered stereotypes. As a result, AI reproduces these biases by associating domestic tasks with women, simply reflecting the unequal way domestic work is represented online and in media. 3. Impact of Content Creation on AI: Dr Pillay suggests that content creators can challenge and reshape these biases by producing and sharing more diverse, gender-neutral representations of domestic work. By doing so, they can indirectly influence AI's training datasets and help promote more equitable gender representations in future AI-generated content. 4. The Power of AI for Change: despite the limitations and biases of current AI systems, the article emphasises the potential of AI to be a tool for positive change. Dr Pillay highlights her own use of AI in creating a film to raise awareness about urinary incontinence, demonstrating how AI can be a valuable resource in addressing social issues while offering new creative possibilities. Overall, the article underscores the need for more thoughtful and inclusive content creation to challenge and alter the gendered assumptions built into AI models and the digital landscape. What do you think? https://lnkd.in/eAnR5xhE #ai #artificialintelligence #genderbias #unconsciousbias #edi #diversity #technology

AI is SEXIST?! Shocking New Report Reveals AI Tools DISCRIMINATE Against Women! Your AI might be sexist! Study finds AI tools promote gender stereotypes & bias. Is your AI secretly judging you? Read more!** #AI #GenderBias #TechBias #FutureofTech