Artificial intelligence (AI) is considered one of the most revolutionary developments in human history, and the world has already witnessed its transformative capabilities. Not surprisingly, AI-based innovations are powering some of the most cutting-edge solutions we use in our daily lives. Today, AI empowers organizations, governments and communities to build a high-performing ecosystem to serve the entire world. Its profound impact on human lives is solving some of the most critical challenges faced by society. Here are a few innovations for social causes that I find most notable.

Welcome to the fourth edition of the AI Index Report. This year we significantly expanded the amount of data available in the report, worked with a broader set of external organizations to calibrate our data, and deepened our connections with the Stanford Institute for Human-Centered Artificial Intelligence (HAI). The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. The report aims to be the most credible and authoritative source for data and insights about AI in the world.

Automated decision systems are increasingly used for consequential decision making -- for a variety of reasons. These systems often rely on sophisticated yet opaque models, which do not (or hardly) allow for understanding how or why a given decision was arrived at. This is not only problematic from a legal perspective, but non-transparent systems are also prone to yield undesirable (e.g., unfair) outcomes because their sanity is difficult to assess and calibrate in the first place. In this work, we conduct a study to evaluate different attempts of explaining such systems with respect to their effect on people's perceptions of fairness and trustworthiness towards the underlying mechanisms. A pilot study revealed surprising qualitative insights as well as preliminary significant effects, which will have to be verified, extended and thoroughly discussed in the larger main study.

It is challenging to deal with censored data, where we only have access to the incomplete information of survival time instead of its exact value. Fortunately, under linear predictor assumption, people can obtain guaranteed coverage for the confidence band of survival time using methods like Cox Regression. However, when relaxing the linear assumption with neural networks (e.g., Cox-MLP \citep{katzman2018deepsurv,kvamme2019time}), we lose the guaranteed coverage. To recover the guaranteed coverage without linear assumption, we propose two algorithms based on conformal inference. In the first algorithm \emph{WCCI}, we revisit weighted conformal inference and introduce a new non-conformity score based on partial likelihood. We then propose a two-stage algorithm \emph{T-SCI}, where we run WCCI in the first stage and apply quantile conformal inference to calibrate the results in the second stage. Theoretical analysis shows that T-SCI returns guaranteed coverage under milder assumptions than WCCI. We conduct extensive experiments on synthetic data and real data using different methods, which validate our analysis.

The reason for focusing on this area of bias is regarded as important by some enterprises. This is because unconscious bias is often easy to miss. Moreover, unconscious bias is often seen to be far more pervasive in the workplace than blatant discrimination. According to some researchers, unconscious bias can be blamed for lower wages, less opportunities for advancement and high turnover. Unconscious biases are types of social stereotypes held by members of one group about other groups of people.

An academic and a lawyer have teamed up to develop a robot lawyer, which, if successful, will make legal advice affordable to people from all backgrounds, while revolutionising the legal sector. Robots could take on significant parts of a lawyer's work, reducing the costs and barriers to access to legal services for everyone, rather than just those who can afford the high costs. The project, at the University of Bradford, is initially working on a machine learning-based application to provide immigration-related legal advice, but if successful, it could be replicated across the legal sector. The idea has received government backing in the form of a £170,000 grant from Innovate UK Knowledge Transfer Partnerships. Legal firm AY&J Solicitors is providing a further £70,000 as well as the vital knowledge of lawyers.

Despite major disruptions from the ongoing COVID-19 pandemic, global investment in AI technologies grew by 40 percent in 2020 to $67.9 billion, up from $48.8 billion in 2019, as AI research and use continues to boom across broad segments of bioscience, healthcare, manufacturing and more. The figures, compiled as part of Stanford University's Artificlal Intelligence Index Report 2021 on the state of AI research, development, implementation and use around the world, help illustrate the continually changing scope of the still-maturing technology. The 222-page AI Index 2021 report, touted as the school's fourth annual study of AI impact and progress, was released March 3 by Stanford's Institute for Human-Centered Artificial Intelligence. The report provides a detailed portrait of the AI waterfront last year, including increasing AI investments and use in medicine and healthcare, China's growth in AI research, huge gains in AI capabilities across industries, concerns about diversity among AI researchers, ongoing debates about AI ethics and more. "The impact of AI this past year was both societal and economic, driven by the increasingly rapid progress of the technology itself," AI Index co-chair Jack Clark said in a statement.

We've seen widespread disruption, change and uncertainty in every sphere of business. Yet, chaotic, unstable times tend to also bring great leaps forward in terms of technology and innovation. In 2020, I've seen numerous enterprises discover just how much AI and ML tools can help their organization remain stable and even continue to grow despite the turmoil rolling through the markets. But this growth comes with the necessity to assure the health of ML models in production to avoid drifts, biases and anomalies. While AI adoption has taken a giant leap forward, we've learned that ML models need to be adaptable and robust.

Artificial intelligence systems, which are designed with a capability to learn from the data presented to them, are used throughout society. These systems are used to screen loan applicants, make sentencing recommendations for criminal defendants, scan social media posts for disallowed content and more. Because these systems don't assign meaning to their complex learned correlation network, they can learn associations that don't equate to causality, resulting in non-optimal and indefensible decisions being made. In addition to making decisions that are sub-optimal, these systems may create legal liability for their designers and operators by learning correlations that violate anti-discrimination and other laws regarding what factors can be used in different types of decision making. This paper presents the use of a machine learning expert system, which is developed with meaning-assigned nodes (facts) and correlations (rules). Multiple potential implementations are considered and evaluated under different conditions, including different network error and augmentation levels and different training levels. The performance of these systems is compared to random and fully connected networks.

I recently read an article about ageism, and how companies are struggling to effectively manage it. A short time later I read about Artificial Intelligence (AI), and how it will make Talent Acquisition (TA) more efficient. So, I started thinking…how will AI and ageism coexist? Bias is practiced every day by everyone, starting with personal preference for morning coffee and what goes in it, to our taste in cars (Tesla), to what to buy on Amazon, and more. We also have strong biases in technology preference: Mac or PC, iPhone or Galaxy, Samsung, or LG?