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Machine Learning ExplainedSara Brownhttps://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explainedarticleGREAT general source to point to for beginners. dive into ai and the different types of machine learning. the perks, use cases and limitationsAI, explainaility“I don’t think anyone can afford not to be aware of what’s happening.”

That includes being aware of the social, societal, and ethical implications of machine learning. “
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The Return on Investment in AI Ethics: A Holistic FrameworkIBM & Notre Damehttps://arxiv.org/pdf/2309.13057.pdfacademic paperWe propose a Holistic Return on Ethics (HROE) framework for understanding the return on organizational investments in artificial intelligence (AI) ethics efforts. This framework is useful for organizations that wish to quantify the return for their investment decisions. The framework identifies the direct economic returns of such investments, the indirect paths to return through intangibles associated with organizational reputation, and real options associated with capabilities. The holistic framework ultimately provides organizations with the competency to employ and justify AI ethics investments.AI, ethics, ROI"There are three paths to understanding the impact of investments in AI ethics with regards to stakeholders: the direct path through economic return, and indirect paths through capabilities and reputation "/"If organizations make investments to improve the environmental, social, or governance components of their carbon footprint, they will not only impact the ESG score, but also potentially impact the perspectives of a variety of stakeholders. More generally, through improvements in their indicators, organizations will impact stakeholder perceptions, thus increasing brand loyalty, trust, and satisfaction" / "Millennials and Gen-Z are motivated to work for “socially responsible employers” that “prioritize purpose (Aziz, 2021)."
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What Is the Difference Between AI Ethics, Responsible AI, and Trustworthy AI? We ask our Responsible AI LeadsTyler Wells Lynchhttps://ai.northeastern.edu/what-is-the-difference-between-ai-ethics-responsible-ai-and-trustworthy-ai-we-ask-our-responsible-ai-leads/articleThis article argues that Responsible AI, though not a perfect label, is less problematic than ethcial or trustworthy AI. Responsible AI, ethics“Trust is really the outcome of what we want to do,” she says. “Our focus should be on the system itself, and not on the feeling it eventually—hopefully—evokes.”/ “AI can have an ethical outcome or an unethical outcome,” Cansu says. “It can incorporate value judgments, but it's not an ethical being with intent. It's not a moral agent... systems are only as ethical as the intent of the people who create them.”/
We are trying to emphasize that responsible AI is about creating structures and roles for developing AI responsibly, and that responsibility will always lie in these structures and the people who design the systems.”
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2024 BRIEF overview on LLM / "foundation models" / 2023's version of "AI"Joanna Brysonhttps://joanna-bryson.blogspot.com/2024/02/2024-brief-overview-on-llm-foundation.html?m=1articleThis article dives into the pros and cons of LLMs and GenAI being all the rage in the new AI summer. The author is clearly super done with all the cap and raises several valid pointsGen AI, LLMs, AI ethics, sustainability, Web search is still the best way to find the highest quality (including most dangerous) publicly-available human knowledge. Generative AI, while creatively interpolating found results, does not innovate truth – it just creates a lot of conjectures very quickly. Sometimes these conjectures are called "hallucinations."//These are systems of prediction. Predictions made from insufficient data will always be random. The problem is that the same thing that makes these systems really useful (that they are learning about culture e.g. language at many different levels simultaneously) also ensures that they are deeply inhuman.//is overemphasis on generative AI foundation models a regulatory threat?
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Modelling the MindMichael Woolrigdehttps://www.the-tls.co.uk/articles/a-brief-history-of-intelligence-max-bennett-putting-ourselves-back-in-the-equation-george-musser-book-review-michael-wooldridge/articleWhy AI fails to crack the code of human consciousnessconciousness, AI, quantum, physics, evolution"Silicon Valley tries to deliver advantages by throwing ever more data and computer power at the problem. From a scientific point of view I find the latter approach somehow unsatisfactory: it amounts to solving the problem of intelligence by brute force rather than scientific insight. But that is where all the attention is in AI – and all the money."
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Micorsoft is draining Arizona's WaterMaggie Harrison Duprehttps://futurism.com/the-byte/microsoft-arizona-water-aiarticle
A massive Microsoft data center in Goodyear, Arizona is guzzling the desert's water to support its cloud computing and AI efforts, The Atlanticreports.
AI, ethics, sustainability, environmental impact
Powering AI demands an incredible amount of energy. Worsening AI's massive environmental footprint is the fact that it also consumes a mind-boggling amount of water. AI pulls enough electricity from data centers that they risk overheating, so to mitigate that risk, engineers use water to cool the servers back down.
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Does AGI need to be concious?Peter Vosshttps://medium.com/@petervoss/does-an-agi-need-to-be-conscious-17cf1e8d2400articleconciousness, AGI"Consciousness at its base refers to awareness: Is the entity absorbing stimuli from the environment? Is it responding? However, we usually only apply these terms to living things"// "One can actually bypass much of the definitional debate by concentrating on what kind of consciousness-like properties an AGI needs."
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Tips and Resources for Introducing Students to Artificial IntelligenceJorge Valenzuelahttps://www.edutopia.org/article/tips-and-resources-for-introducing-students-to-artificial-intelligence/toolthis is an AMAZING collection of projects, resources and pedagodgy tips for teaching AIAI, education
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The Ethics of AIUniversity of Helsinkihttps://ethics-of-ai.mooc.ficourseThe Ethics of AI is a free online course created by the University of Helsinki. The course is for anyone who is interested in the ethical aspects of AI – we want to encourage people to learn what AI ethics means, what can and can’t be done to develop AI in an ethically sustainable way, and how to start thinking about AI from an ethical point of view.AI, ethics
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Deconstructing BERT, Part 2: Visualizing the Inner Workings of AttentionJesse Vighttps://towardsdatascience.com/deconstructing-bert-part-2-visualizing-the-inner-workings-of-attention-60a16d86b5c1article
A new visualization tool shows how BERT forms its distinctive attention patterns.
NLP, explainable AI, transformers, BERT
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Starting & Running a Successful Solo Consulting Practice
Cliff Ennicohttps://www.youtube.com/watch?v=2YF0_N7DuwAvideoconsulting
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Model Cards for Model ReportingMargaret Mitchel et al. https://arxiv.org/pdf/1810.03993.pdfacademic paperTrained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, edu- cation, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their perfor- mance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phe- notypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type [15]) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation pro- cedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two super- vised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related artificial intelligence technology, increasing transparency into how well artificial intelligence technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation.
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Word embeddingstensor flowhttps://www.tensorflow.org/text/guide/word_embeddingstutorialNLP,word embeddings
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Training, Visualizing, and Understanding Word Embeddings: Deep Dive Into Custom Datasetsneptune.aihttps://neptune.ai/blog/word-embeddings-deep-dive-into-custom-datasetstutorialNLP,word embeddings
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The Prosperity Paradox: How Innovation Can Lift Nations out of Poverty free pdfClayton Christenssenhttps://pdfroom.com/books/the-prosperity-paradox/DkgVenoqd9Bbookconsulting
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AI Ethics: Global Perspectivevarious contributorshttps://aiethicscourse.org/modulesvideoAI, ethics
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ETHICS GUIDELINES FOR TRUSTWORTHY AIEuropean Commisionhttps://op.europa.eu/en/publication-detail/-/publication/d3988569-0434-11ea-8c1f-01aa75ed71a1report
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Everyday Ethics for Artificial IntelligenceIBMhttps://www.ibm.com/watson/assets/duo/pdf/everydayethics.pdfebook
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Common ethical challenges in AICouncil of Europehttps://www.coe.int/en/web/bioethics/common-ethical-challenges-in-aiebook
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The ethics of artificial intelligence: Issues and initiativesEuropean Parlimenthttps://www.europarl.europa.eu/RegData/etudes/STUD/2020/634452/EPRS_STU(2020)634452_EN.pdfreport
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A high-level overview of AI ethics
Emre Kazim1 and Adriano Soares Koshiyama
https://discovery.ucl.ac.uk/id/eprint/10135999/1/1-s2.0-S2666389921001574-main.pdfreport
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Ethical Considerations in Artificial Intelligence CoursesBurton et al.https://arxiv.org/pdf/1701.07769.pdfacademic paper
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AI ethics: How marketers should embrace innovation responsiblyJamia Kenanhttps://sproutsocial.com/insights/ai-ethics/article
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explaining explanations in AIMittelstadt et a.https://arxiv.org/pdf/1811.01439.pdfacademic paper
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Equality, diversity and inclusionAlan Turing Institutehttps://www.turing.ac.uk/about-us/equality-diversity-and-inclusionwebsite/organization
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Universal Declaration of Human RightsUnited Nationshttps://www.un.org/en/about-us/universal-declaration-of-human-rightsdocument
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Centre for the Fourth Industrial RevolutionWorld Economic Forumhttps://centres.weforum.org/centre-for-the-fourth-industrial-revolution/homewebsite/organization
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AI Use Case ExplorerAWShttps://aiexplorer.aws.amazon.com/?lang=entool
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Make your first AI in 15 minutesKhanradCoderhttps://www.youtube.com/watch?v=z1PGJ9quPV8tutorial
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Make your first AI in 15 minutesKhanradCoder/ Adam Eubankshttps://colab.research.google.com/drive/15SjXH0xObngJdB9T_xvjEVYUx61mbKYMtutorial code
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SYSTEMS THINKING: WHAT, WHY, WHEN, WHERE, AND HOW?Michael Goodmanhttps://thesystemsthinker.com/systems-thinking-what-why-when-where-and-how/article
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National AI Research ResourceUS govhttps://new.nsf.gov/focus-areas/artificial-intelligence/nairrtool
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Generative AI for Beginnersmicrosofthttps://github.com/microsoft/generative-ai-for-beginnersgithub repo
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making AI more interpretableKhanradhttps://www.youtube.com/watch?v=04pOT5lmiK0videoToday, AI models wield tremendous power, yet they are often biased, inaccessible, and uninterpretable. We often deploy these models blindly, feeding data into a black box, and crossing our fingers in hopes that the outcomes align with our values. This makes it hard to trust AI models that can often hallucinate or reveal biases within the datasets they’ve been fed. On top of that, very few individuals even have the technical know-how and resources necessary to leverage this technology. This has created a gap between those who have access to AI and those who don’t. As this gap increases, questions of AI bias and alignment only become more important. As AI's role in our lives expands, so do its imperfections, forcing us to confront its issues of bias, explainability, and restricted access. This needs to change. It’s time to democratize AI, uncover the Blackbox, and make the most important technologies of our day accessible to everyone. Through Symbolic Regression and an easy-to-access machine learning dashboard, we can make AI more accessible and interpretable for all.explainable AI, XAI, interpretability, AI, ethics
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Research and development grant programsSBAhttps://www.sba.gov/funding-programs/grantstoolgrant
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America's Seed FundSBAhttps://www.sbir.govtoolsmall business
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Categorical imperativewikihttps://en.wikipedia.org/wiki/Categorical_imperativewikipage
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Built-in Bias: What Are the Ethical Responsibilities of Developers?Stephanie Vaughnhttps://speakerdeck.com/slvaughn/built-in-bias-what-are-the-ethical-responsibilities-of-developers?slide=42slides
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devlandMiles Dotson & cohttps://www.devland.us/studio-xstudio
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Systems Archetypes IDaniel H Kimhttps://thesystemsthinker.com/wp-content/uploads/2016/03/Systems-Archetypes-I-TRSA01_pk.pdfebooksystems thinking, management consulting
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Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Gautier Izacard Edouard Grave, Facebook
https://arxiv.org/pdf/2007.01282.pdfacademic paperLLM, retrieval, ethical AI
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Generative AI Guidelines for TechSouphttps://pages.techsoup.org/hubfs/Downloads/2023.08.16_GenerativeAIGuidelines_TechSoup.pdfguide
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