Fiddler at O’Reilly AI Conference Sept 11 & 12

The San Jose O’Reilly Artificial Intelligence conference is almost upon us. From top researchers and developers to CxOs innovating in AI, we’ll hear about the latest innovations in machine learning and AI. 

Fiddler’s very own Ankur Taly, Head of Data Science, will be speaking on September 12 on Explaining Machine Learning Models. Ankur is well-known for his contribution to developing and applying Integrated Gradients  — a new interpretability algorithm for Deep Neural Networks. He has a broad research background and has published in several areas including Computer Security and Machine Learning. We hope to see you at his session!

At Fiddler, our mission is to enable businesses of all sizes to unlock the AI black box and deliver trustworthy and responsible AI experiences. Come chat with us about: 

  • Risks associated with not having visibility into model outputs
  • Most innovative ways to understand, manage, and analyze your ML models
  • Importance of Explainable AI and providing transparent and reliable experiences to end users

Schedule a time to connect with us

If you’d like to set up a meeting beforehand, then fill out this meeting form and we’ll be in touch. We’re excited to chat with you!

Where to find us

September 11 & 12

We’ll be in the Innovator Pavilion: Booth #K10, so stop by and say hi! 

September 12

Join Ankur Taly, our Head of Data Science, at his session on Explaining Machine Learning Models – 2:35pm – 3:15pm, Sep 12 / LL21 A/B

As machine learning models get deployed to high stakes tasks like medical diagnosis, credit scoring, and fraud detection, an overarching question that arises is – why did the model make this prediction? This talk will discuss techniques for answering this question, and applications of the techniques in interpreting, debugging, and evaluating machine learning models.

See you next week!

Welcome Anusha Sethuraman!

We’re excited to introduce Anusha Sethuraman, the newest member of our team. Anusha joins us as our Head of Product Marketing.

Anusha comes from a diverse product marketing background across startups and enterprises, most recently on Microsoft’s AI Thought Leadership team where she spearheaded the team’s storytelling strategy with stories being featured in CEO and exec-level Keynotes. Before this, she was at Xamarin (acquired by Microsoft) leading enterprise product marketing where she launched Xamarin’s first decision-maker event and was instrumental in creating the integrated Microsoft + Xamarin story. And prior to that, she was at New Relic (pre-IPO) leading product marketing for New Relic’s mobile monitoring product.

Anusha believes in a world where AI is responsible, ethical, and understandable. In her own words:

“The idea of democratizing AI is great, but even better – democratizing AI that has ethics and responsibility inbuilt. Today’s AI-powered world is nowhere close to being trustworthy: we still run into everyday instances of not knowing the why and how behind the decisions AI generates. Fiddler’s bold ambitions to create a world where technology is built responsibly, where humanity is not only putting AI to the best use possible across all industries and scenarios but creating this ethically and responsibly right from the start is something I care about deeply.  I’m very excited to be joining Fiddler to lead Product Marketing and work towards building an AI-powered world that is understandable, transparent, explainable, and secure.”

Anusha Sethuraman

Regulations To Trust AI Are Here. And It’s a Good Thing.

This article was previously posted on Forbes.

As artificial intelligence (AI) adoption grows, so do the risks of today’s typical black-box AI. These risks include customer mistrust, brand risk and compliance risk. As recently as last month, concerns about AI-driven facial recognition that was biased against certain demographics resulted in a PR backlash. 

With customer protection in mind, regulators are staying ahead of this technology and introducing the first wave of AI regulations meant to address AI transparency. This is a step in the right direction in terms of helping customers trust AI-driven experiences while enabling businesses to reap the benefits of AI adoption.

This first group of regulations relates to the understanding of an AI-driven, automated decision by a customer. This is especially important for key decisions like lending, insurance and health care but is also applicable to personalization, recommendations, etc.

The General Data Protection Regulation (GDPR), specifically Articles 13 and 22, was the first regulation about automated decision-making that states anyone given an automated decision has the right to be informed and the right to a meaningful explanation. According to clause 2(f) of Article 13:

“[Information about] the existence of automated decision-making, including profiling … and … meaningful information about the logic involved [is needed] to ensure fair and transparent processing.”

One of the most frequently asked questions is what the “right to explanation” means in the context of AI. Does “meaningful information about the logic involved” mean that companies have to disclose the actual algorithm or source code? Would explaining the mechanics of the algorithm be really helpful for the individuals? It might make more sense to provide information on what inputs were used and how they influenced the output of the algorithm. 

For example, if a loan application or insurance claim is denied using an algorithm or machine learning model, under Articles 13 and 22, the loan or insurance officer would need to provide specific details about the impact of the user’s data to the decision. Or, they could provide general parameters of the algorithm or model used to make that decision.

Similar laws working their way through the U.S. state legislatures of Washington, Illinois and Massachusetts are

  • WA House Bill 1655, which establishes guidelines for “the use of automated decision systems in order to protect consumers, improve transparency, and create more market predictability.”
  • MA Bill H.2701, which establishes a commission on “automated decision-making, artificial intelligence, transparency, fairness, and individual rights.”
  • IL HB3415, which states that “predictive data analytics in determining creditworthiness or in making hiring decisions…may not include information that correlates with the race of zip code of the applicant.”

Fortunately, advances in AI have kept pace with these needs. Recent research in machine learning (ML) model interpretability makes compliance to these regulations feasible. Cutting-edge techniques like Integrated Gradients from Google Brain along with SHAP and LIME from the University of Washington enable unlocking the AI black box to get meaningful explanations for consumers. 

Ensuring fair automated decisions is another related area of upcoming regulations. While there is no consensus in the research community on the right set of fairness metrics, some approaches like equality of opportunity are already required by law in use cases like hiring. Integrating AI explainability in the ML lifecycle can also help provide insights for fair and unbiased automated decisions. Assessing and monitoring these biases, along with data quality and model interpretability approaches, provides a good playbook towards developing fair and ethical AI.

The recent June 26 US House Committee hearing is a sign that financial services need to get ready for upcoming regulations that ensure transparent AI systems. All these regulations will help increase trust in AI models and accelerate their adoption across industries toward the longer-term goal of trustworthy AI.