AI ethics issues

Chatbots and other artificial intelligence technologies that interact with humans raise ethics questions that enterprises can't ignore. Is it acceptable to present a bot as a human? How do you eliminate AI bias? Do organizations need an AI code of ethics? Read what experts have to say on AI ethics issues, whether companies should self-impose an AI code of ethics and more.

January 15, 2021 15 Jan'21
Diverse talent pools and data sets can help solve bias in AI

Bringing historically underrepresented employees into critical parts of the design process while creating an AI model can reduce or eliminate bias in that model.
January 13, 2021 13 Jan'21
The power and limitations of enterprise AI

A panel at CES 2021, held virtually this year, discusses the areas in which modern-day AI and automation shine, and where they still struggle.
January 12, 2021 12 Jan'21
White House issues guiding principles for AI regulations

While the recommendations for AI regulation in the White House memo are loose, they appear to show the government is beginning to embrace AI, and could be a start toward oversight.
January 08, 2021 08 Jan'21
Doubts about Trump video show how hard deepfakes are to detect

Some Twitter users insist that a recent video of President Trump admitting his defeat in the election is a deepfake, although there's no proof. Deepfakes are difficult to detect.

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artificial intelligence

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The peaks and pitfalls of hyper-personalization marketing

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Weigh the pros and cons of technologies, products and projects you are considering.

Bias in machine learning examples: Policing, banking, COVID-19

Human bias, missing data, data selection, data confirmation, hidden variables and unexpected crises can contribute to distorted machine learning models, outcomes and insights. Continue Reading
In-depth guide to machine learning in the enterprise

Enterprises are adopting machine learning technologies at rapid rates. In this machine learning guide, we break down what you need to know about this transformative technology. Continue Reading
5 AI risks businesses must confront and how to address them

Some risks associated with implementing AI systems are familiar to enterprise leaders; these five are unique to AI. Here's how to address them. Continue Reading

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Building trustworthy AI is key for enterprises

Organizations need to focus on transparency in models, ethical procedures and responsible AI in order to best comply with guidelines for developing trustworthy AI systems. Continue Reading
Machine learning and bias concerns weigh on data scientists

Data scientists are forever vigilant in their desire to identify and eliminate the many forms of bias that can compromise the credibility of machine learning models. Continue Reading
Responsible AI champions human-centric machine learning

Encompassing ethics, transparency and human centricity, responsible AI is an effective approach to deploying machine learning models and achieving actionable insights. Continue Reading

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Data science's ongoing battle to quell bias in machine learning

Machine learning expert Ben Cox of H2O.ai discusses the problem of bias in predictive models that confronts data scientists daily and his techniques to identify and neutralize it. Continue Reading
Here's how one lawyer advises removing bias from AI

Avoiding bias in AI applications is one of the central challenges in using the technology. Here's some advice on deploying AI technologies in a way that is fair. Continue Reading
Supervise data and open the black box to avoid AI failures

As AI blooms, marketers and vendors are quick to highlight easy positive use cases. But implementation can -- and has -- gone wrong in cases that serve as warnings for developers. Continue Reading