I was reading two articles this week on MIT Technology Review about the difficulties of explaining the decision-making of advanced algorithms that uses AI. This explanation is fundamental as our life's become more intertwined in ways that sometimes we do not even realized. From self-driving cars, who's approved for a loan, and personalized medicine the issue of a methodology to explain the outputs of AI is becoming to the forefront due to liability issues. After 20 years implementing advanced algorithms filed and 12 years in the legal profession I have come with a methodology that help explaining deep learning results in such a way that is understood in layman's terms. Below is how I would create a minimum viable product (MVP) to develop an application to explain AI outputs.

The 1st National workshop on Smart Farming and Data Analytics took place at Maynooth University in Ireland on June 12, 2019. The workshop included two invited keynote presentations, invited talks and breakout group discussions. The workshop attracted in the order of 50 participants, consisting of a mixture of computer scientists, general scientists, farmers, farm advisors, and agricultural business representatives. This allowed for lively discussion and cross-fertilization of ideas. And showed the significant interest in the smart farming domain, the many research challenges faced in the space and the potential for data analytics and information retrieval here.

A new patent granted to Microsoft by the United States Patent and Trademark Office (USPTO) reveals that the company is working on conversational agents that mirror users' conversational style and/or facial expressions. The patent - Linguistic Style Matching Agent – was granted to Microsoft on September 3, 2020, and credits Daniel J McDuff, Kael R. Rowan, Mary P Czerwinski, Deepali Aneja, and Rens Hoegen as inventors. With advances in speech recognition and generative dialogue models, conversational interfaces like chatbots and virtual agents are becoming increasingly popular. While such natural language interactions have led to an evolution in human-computer interactions, the communication is mostly monotonic and constrained. These conversations, therefore, end up being only transactional and are not very natural.

Human race suffers from the God Complex. Art and science strive to achieve recreate the human form, thought pattern, aesthetics, and ethics. Can we replicate the human intellect by making machines think for themselves? Artificial intelligence does not face the moral dilemma of making choices that fall in the grey area, it is binary in its output. The concept of GIGO – garbage in, garbage out holds in the case of AI too.

We present HDP-VFL, the first hybrid differentially private (DP) framework for vertical federated learning (VFL) to demonstrate that it is possible to jointly learn a generalized linear model (GLM) from vertically partitioned data with only a negligible cost, w.r.t. training time, accuracy, etc., comparing to idealized non-private VFL. Our work builds on the recent advances in VFL-based collaborative training among different organizations which rely on protocols like Homomorphic Encryption (HE) and Secure Multi-Party Computation (MPC) to secure computation and training. In particular, we analyze how VFL's intermediate result (IR) can leak private information of the training data during communication and design a DP-based privacy-preserving algorithm to ensure the data confidentiality of VFL participants. We mathematically prove that our algorithm not only provides utility guarantees for VFL, but also offers multi-level privacy, i.e. DP w.r.t. IR and joint differential privacy (JDP) w.r.t. model weights. Experimental results demonstrate that our work, under adequate privacy budgets, is quantitatively and qualitatively similar to GLMs, learned in idealized non-private VFL setting, rather than the increased cost in memory and processing time in most prior works based on HE or MPC. Our codes will be released if this paper is accepted.

Decision making in crucial applications such as lending, hiring, and college admissions has witnessed increasing use of algorithmic models and techniques as a result of a confluence of factors such as ubiquitous connectivity, ability to collect, aggregate, and process large amounts of fine-grained data using cloud computing, and ease of access to applying sophisticated machine learning models. Quite often, such applications are powered by search and recommendation systems, which in turn make use of personalized ranking algorithms. At the same time, there is increasing awareness about the ethical and legal challenges posed by the use of such data-driven systems. Researchers and practitioners from different disciplines have recently highlighted the potential for such systems to discriminate against certain population groups, due to biases in the datasets utilized for learning their underlying recommendation models. We present a study of fairness in online personalization settings involving the ranking of individuals. Starting from a fair warm-start machine-learned model, we first demonstrate that online personalization can cause the model to learn to act in an unfair manner if the user is biased in his/her responses. For this purpose, we construct a stylized model for generating training data with potentially biased features as well as potentially biased labels and quantify the extent of bias that is learned by the model when the user responds in a biased manner as in many real-world scenarios. We then formulate the problem of learning personalized models under fairness constraints and present a regularization based approach for mitigating biases in machine learning. We demonstrate the efficacy of our approach through extensive simulations with different parameter settings. Code: https://github.com/groshanlal/Fairness-Aware-Online-Personalization

An artificial intelligence (AI) technology made by a firm co-founded by billionaire Elon Musk has won praise for its ability to generate coherent stories, novels and even computer code but it remains blind to racism or sexism. GPT-3, as Californian company OpenAI's latest AI language model is known, is capable of completing a dialogue between two people, continuing a series of questions and answers or finishing a Shakespeare-style poem. Start a sentence or text and it completes it for you, basing its response on the gigantic amount of information it has been fed. This could come in useful for customer service, lawyers needing to sum up a legal precedent or for authors in need of inspiration. While the technology is not new and has not yet learnt to reason like a human mind, OpenAI's latest offering has won praise for the way its text resembles human writing.

There is an interesting link between critical theory and certain genres of literature that may be of interest to the current debate on AI ethics. While critical theory generally points out certain deficiencies in the present to criticize it, futurology and literary genres such as Cyberpunk, extrapolate our present deficits in possible dystopian futures to criticize the status quo. Given the great advance of the AI industry in recent years, an increasing number of ethical matters have been raised and debated, usually in the form of ethical guidelines and unpublished manuscripts by governments, the private sector, and academic sources. However, recent meta-analyses in the field of AI ethics have raised important questions such as: what is being omitted from published ethical guidelines? Does AI governance occur inclusively and diversely? Is this form of "ethics", based on soft rules and principles, efficient? In this study, I would like to present aspects omitted or barely mentioned in the current debate on AI ethics and defend the point that applied ethics should not be based on creating only soft versions of real legislation, but rather on criticizing the status quo for everything of value that is disregarded.

UNESCO just released its new report on Artificial Intelligence and Gender Equality, which sets forth proposed elements of a Framework on Gender Equality and AI for further consideration, discussion and elaboration amongst various stakeholders. Advancing gender equality through education, the sciences, culture, information and communication lies at the heart of UNESCO's mandate, with Gender Equality constituting one of the two Global Priorities of the Organization since 2008. UNESCO is therefore keen to adopt a gender equality lens in its ongoing work on artificial intelligence in all its programme areas. Research, including UNESCO's 2019 report I'd Blush if I Could: closing gender divides in digital skills through education, unambiguously shows that the gender biases found in AI training data sets, algorithms and devices have the potential of spreading and reinforcing harmful gender stereotypes. These gender biases risk further stigmatizing and marginalizing women on a global scale.

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