More Views and Education on AI

Photo by  Aaron Burden

Photo by Aaron Burden

  • Last week, the OECD issued its “Principles on Artificial Intelligence” and said: “AI systems should be designed in a way that respects the rule of law, human rights, democratic values and diversity, and they should include appropriate safeguards – for example, enabling human intervention where necessary – to ensure a fair and just society.

  • Separately, the Brookings Institution released Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms” and said:

  • “Algorithms are harnessing volumes of macro- and micro-data to influence decisions affecting people in a range of tasks, from making movie recommendations to helping banks determine the creditworthiness of individuals.

  • Acknowledging the possibility and causes of bias is the first step in any mitigation approach.

  • “It’s important for algorithm operators and developers to always be asking themselves: Will we leave some groups of people worse off as a result of the algorithm’s design or its unintended consequences?“

  • Finally, the Dimensional Research reportArtificial Intelligence and Machine Learning Projects Are Obstructed by Data Issues” said:

  • 8 out of 10 companies indicate that training AI/ML algorithms is more challenging than they expected.

  • 96% of companies surveyed stated they have run into problems with data quality, labeling required to train the AI, and building model confidence." 

OUR TAKE

  • Well designed AI/ML applications can provide great insights - but biases can amplify negative outcomes in various areas including online recruitment, online marketing, facial recognition and criminal justice.

  • As organizations face the challenge of acquiring and preparing the data - the IT adage “garbage in, garbage out" applies to AI/ML as well.

  • To learn more about AI, check out “AI For Everyone” by Andrew Ng (Coursera). Very accessible content. Ng is Co-founder of Coursera, an Adjunct Professor of Computer Science at Stanford University, founded “Google Brain” project, led Baidu's AI Group

  • Additional AI resources are here.

Paul Dravis