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.

May 14, 2021 14 May'21
Biden sets stage for national AI strategy

Biden's focus on AI includes funding research and development, manufacturing chips in the U.S. and preparing a workforce to use AI tools.
April 21, 2021 21 Apr'21
IBM Watson updates focus on data privacy and explainability

Updates include new federated learning capabilities within IBM Watson Studio and more insights into how predictions are made in IBM Planning Analytics with Watson.
April 09, 2021 09 Apr'21
Check model accuracy with Facebook AI's new data set

Facebook paid people for their likenesses in its new Casual Conversations data set. The data set can help researchers check for bias in their AI models.
April 07, 2021 07 Apr'21
Research manager resigns amid Google AI ethics controversy

Prominent Google AI research manager Samy Bengio resigns from the company following months of internal discord and the termination of two AI researchers.

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9 top AI and machine learning trends for 2021

Tiny ML, multi-modal learning, responsible AI -- learn about the top trends in AI for 2021 and how they promise to transform how business gets done. Continue Reading
5 ways AI bias hurts your business

A biased AI system can lead businesses to produce skewed, harmful and even racist predictions. It's important for enterprises to understand the power and risks of AI bias. Continue Reading
The top 5 benefits of AI in banking and finance

The strategic deployment of AI in banking and finance can bring substantial benefits. Learn about how AI tools are transforming financial services and the risks to be mindful of. Continue Reading

Learn to apply best practices and optimize your operations.

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
10 steps to achieve AI implementation in your business

Maximizing the value of insights into your business, industry and competition requires a thoughtful, creative, experimental, incremental and team approach to deploying AI. Continue Reading
How to detect bias in existing AI algorithms

While enterprises can't eliminate bias from their data, they can significantly reduce bias by establishing a governance framework and employing more diverse employees. Continue Reading

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Combating racial bias in AI

By employing a diverse team to work on AI models, using large, diverse training sets, and keeping a sharp eye out, enterprises can root out bias in their AI models. Continue Reading
Tackling the AI bias problem at the origin: Training data

Though data bias may seem like a back-end issue, the enterprise implications of an AI software using biased data can derail model implementation. Continue Reading
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