Finding Trust and Transparency in Artificial Intelligence
If you work with artificial intelligence technologies you are acutely aware of the implications and consequences for getting it wrong. I wish I could tell you there was a magic wand to be waved that could solve problems like ensuring fairness, removing racial and gender bias, increasing explainability, preventing adversarial attacks, and providing transparency. The reality is that if these are things you care about, and they very well should be, you are going to need to put the work in to understand how to tackle these very important problems. The good news is that there are teams of engineers, data scientists, and researchers at IBM who are building, contributing to and maintaining open source projects and communities to support you in this endeavor.

The Importance of Industry Standards

Globally, more than 50% of enterprise organizations have some form of AI deployment in operation today, with the average company having at least four AI or ML projects running. With this level of rapid adoption, it is imperative that the industry align to ensure that we use this technology in responsible ways. To that end, IBM has joined the LF AI Foundation, underneath the Linux Foundation. The LF AI Foundation supports open source projects within the artificial intelligence, machine learning, and deep learning space. To build trust in the adoption of AI, LF AI has established the Trusted AI Committee. IBM joins a group of member organizations from across the globe to work toward building a set of principles for trustworthy AI and collecting a set of solid public use cases to use as examples of when this work is done right.

As a committee member, I am fortunate to not only share what we are doing at IBM toward this end, but learn from the other member organizations and discover areas for collaboration and knowledge transfer.

Fairness and Bias Mitigation

Defining “what is fair” in the usage of AI is an incredibly difficult thing to do because it is contingent on your particular use case and defined by context. While people may have good intentions with their usage of AI, if they don’t identify systemic racism and cognitive biases that are present in their datasets or the way their models and algorithms are built, they can all too easily perpetuate and AUTOMATE inequalities that exist in society.

The AI Fairness 360 toolkit (AIF360) helps you toward this end. AIF360 is an open source Python library that includes a long list of metrics to help you define and measure individual and group fairness in your models and datasets. Once you’ve got a solid handle on the problem you need to solve you can dive in to the bias mitigation algorithms to actually create some change in the output of your models.

Improving Explainability

For many who want to take advantage of the efficiency gains from AI, the inner workings of machine learning algorithms is a mystery and thus the outcomes from your models are hard to justify or explain. Right now, impressive accuracy is achieved through black box machine learning models, such as deep neural networks and large ensembles, but there are few people who can understand or explain these outcomes. To be able to confidently utilize machine learning for high stakes decisions, explainability and interpretability of the models is essential.

AI Explainability 360 toolkit (AIX360) was built to increase transparency and to allow your machine learning models to explain their decisions. This toolkit includes many ways to explain your machine learning model’s outcomes: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, static vs. interactive; the appropriate choice depends on the platform and audience that needs the explanation.
['Maureen Mcelaney']
['Trustworthy Ai', 'Machine Learning', 'Lf Ai', 'Python', 'Open Source']