We need Ethical Artificial Intelligence

Robotic Process Automation (RPA), Insights: The Productivity Step

The diverse use cases for AI raise ethical and moral questions about how technology is used in a fair and just manner. Artificial intelligence (AI) is doing what the tech-world Cassandras have been predicting for some time: It is sending out curve balls, leaving a trail of misadventures and tricky questions around the ethics of using synthetic intelligence. Sometimes, spotting and understanding the dilemmas AI presents is easy, but often it is difficult to pin down the exact nature of the ethical questions it raises.

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How to avoid ethical missteps by artificial intelligence

Figures standing on money

Artificial intelligence has sometimes fallen into glaring and embarrassing errors. In the industry, you only have to mention the ‘gorilla case’ and everyone will understand that you are referring to the 2015 incident, when the Google Photos AI model providing a description to the images indicated two black people as ‘gorillas’. One of the people involved, developer Jacky Alciné, reported it on Twitter and Google apologised profusely…

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Facebook’s news summarization tool reeks of bad intentions

This week, BuzzFeed News, citing sources familiar with the matter, wrote that Facebook is developing an AI tool that summarizes news articles so that users don’t have to read them. The tool — codenamed “TLDR” in reference to the acronym “too long, didn’t read” — reportedly reduces articles to bullet points and provides narration, as well as a virtual assistant to answer questions.

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Tips for applying an intersectional framework to AI development

y now, most of us in tech know that the inherent bias we possess as humans creates an inherent bias in AI applications — applications that have become so sophisticated they’re able to shape the nature of our everyday lives and even influence our decision-making. The more prevalent and powerful AI systems become, the sooner the industry must address questions like: What can we do to move away from using AI/ML models that demonstrate unfair bias?

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EU human rights agency issues report on AI ethical considerations

The European Union’s Fundamental Rights Agency (FRA) has issued a report on AI which delves into the ethical considerations which must be made about the technology. FRA’s report is titled Getting The Future Right and opens with some of the ways AI is already making lives better—such as helping with cancer diagnosis, and even predicting…

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Stanford and Carnegie Mellon find race and age bias in mobility data that drives COVID-19 policy

Smartphone-based mobility data has played a major role in responses to the pandemic. Describing the movement of millions of people, location information from Google, Apple, and others has been used to analyze the effectiveness of social distancing polices and probe how different sectors of the economy have been affected.

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Identifying and correcting Label Bias in Machine Learning

As machine learning (ML) becomes more effective and widespread it is becoming more prevalent in systems with real-life impact, from loan recommendations to job application decisions. With the growing usage comes the risk of bias – biased training data could lead to biased ML algorithms, which in turn could perpetuate discrimination and bias in society.

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Zest raises $15 million to reduce loan algorithm bias

Zest AI, a company developing AI-powered loan decisioning products, today closed a $15 million funding round led by Insight Partners. A spokesperson says the capital will be used to accelerate Zest’s go-to-market efforts and product R&D.

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Could machine learning help bring marginalized voices into historical archives?

Researchers at the Montreal AI Ethics Institute and Microsoft propose using machine learning to build comprehensive archives that could bridge gaps in cultural understanding, knowledge, and views. They assert that including more voices in archival processes — with the help of machine learning — can have positive effects on communities, particularly those archivists have historically marginalized.

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Researchers claim bias in AI named entity recognition models

Twitter researchers claim to have found evidence of demographic bias in named entity recognition. They say their analysis reveals AI performs better at identifying names from specific groups, and the biases manifest in syntax, semantics, and how word uses vary across linguistic contexts.

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