The advent of big data has raised significant challenges in analysing high-dimensional datasets across various domains such as medicine, ecology, and economics. Functional Data Analysis (FDA) has proven to be a robust framework for addressing these challenges, enabling the transformation of high-dimensional data into functional forms that capture intricate temporal and spatial patterns. However, despite advancements in functional classification methods and very high performance demonstrated by combining FDA and ensemble methods, a critical gap persists in the literature concerning the transparency and interpretability of black-box models, e.g. Functional Random Forests (FRF). In response to this need, this paper introduces a novel suite of explainability tools to illuminate the inner mechanisms of FRF. We propose using Functional Partial Dependence Plots (FPDPs), Functional Principal Component (FPC) Probability Heatmaps, various model-specific and model-agnostic FPCs' importance metrics, and the FPC Internal-External Importance and Explained Variance Bubble Plot. These tools collectively enhance the transparency of FRF models by providing a detailed analysis of how individual FPCs contribute to model predictions. By applying these methods to an ECG dataset, we demonstrate the effectiveness of these tools in revealing critical patterns and improving the explainability of FRF.

Earlier this week, Senate majority leader Chuck Schumer unveiled his SAFE Innovation Framework for artificial intelligence (AI), calling on Congress to take swift, decisive action. Leaders in the AI industry have been calling out for regulation. But Schumer's proposal reveals how difficult it could be in practice for policymakers to regulate a technology that even experts struggle to fully understand. The SAFE Innovation Framework has a number of policy goals: make sure AI systems are secure against cyber attacks, protect jobs, ensure accountability for those deploying AI systems, and defend U.S. democratic values, all without stifling innovation. The part of Schumer's framework which comes closest to making a concrete policy proposal, rather than setting a policy goal, is his call for explainability.

Linh C. Ho has held executive leadership roles for a number of global tech companies and currently serves as Chief Growth Officer at Zelros. It is no surprise that technology as ubiquitous as artificial intelligence (AI) would eventually require ethical guardrails. Just this past fall, the White House announced a Blueprint for an AI Bill of Rights. In it, the administration proposes a five-part framework for companies using automated systems in their operations: effective and safe systems; data privacy; protections against algorithmic discrimination; notice and explanation; and human alternatives, consideration and fallback. Together, the five principles in the Bill of Rights form an overlapping set of backstops--safeguards intended to help keep the American public free from any harm caused by the unchecked use of AI and other emerging technologies.

Have you ever wanted to try Question Answering (QA) models but felt restrained because you needed to write some code to set them up? Have you ever wanted to compare QA models, but a Jupyter Notebook is too inconvenient to compare them? Have you ever wanted to use explainability methods such as saliency maps to explain the outputs, but you don't even know where to start? We have been there too! That's why we built SQuARE: Software for Question Answering Research!

Toronto-Dominion Bank Group is using a software tool built in-house that explains how its artificial-intelligence systems make decisions. The tool, which TD talked about for the first time earlier this month, was developed so that software engineers at the bank could tell its business executives how its systems arrive at conclusions, according to Tomi Poutanen, TD's chief AI officer. It was first deployed in mid-2018.