As Artificial intelligence (AI) and Machine Learning (ML) continue to become more prevalent in mainstream lifestyle and professions, their widespread understanding becomes necessary.

Considering this, Google has taken the initiative called AI Explorables to make the core concepts of machine learning more accessible via a series of interactive explanations. Currently, Google has posted explanations of two fundamental concepts—Hidden Bias and Measuring Fairness. In the coming months, Google plans on posting more ‘Explorables’ on other fairness issues such as the effect of feedback loops on the biases of the AI system.

All in all, with AI Explorables, Google and its People and AI Research team (PAIR) hope to make machine learning more accessible, participatory, and inclusive. PAIR has also written an AI guidebook to help user experience professionals and product managers follow a human-centric approach to AI.

The guidebook’s recommendations are based on data and insights from over a hundred individuals across Google product teams, industry experts and academic research. The guidebook features six chapters on topics such as User Needs + Defining Success, Data Collection + Evaluation, Mental Models and three other subjects. These chapters are also accompanied by worksheets to help turn guidance into action.

Furthermore, PAIR features open-source tools and platforms that make ML models more understandable, trustworthy, and fair. Currently, three tools - What-If tool, Facets and tensor flow.js are features which probe the behaviour of AI models with minimum coding, explore and analyse large machine learning datasets and Develop ML models in Javascript and use them in a browser or via Node.js, respectively.