Every step we take in the digital world leaves behind a record of our behavior; a digital footprint. Research has suggested that algorithms can translate these digital footprints into accurate estimates of psychological characteristics, including personality traits, mental health or intelligence. The mechanisms by which AI generates these insights, however, often remain opaque. In this paper, we show how Explainable AI (XAI) can help domain experts and data subjects validate, question, and improve models that classify psychological traits from digital footprints. We elaborate on two popular XAI methods (rule extraction and counterfactual explanations) in the context of Big Five personality predictions (traits and facets) from financial transactions data (N = 6,408). First, we demonstrate how global rule extraction sheds light on the spending patterns identified by the model as most predictive for personality, and discuss how these rules can be used to explain, validate, and improve the model. Second, we implement local rule extraction to show that individuals are assigned to personality classes because of their unique financial behavior, and that there exists a positive link between the model's prediction confidence and the number of features that contributed to the prediction. Our experiments highlight the importance of both global and local XAI methods. By better understanding how predictive models work in general as well as how they derive an outcome for a particular person, XAI promotes accountability in a world in which AI impacts the lives of billions of people around the world.

There is an increasing convergence between biologically plausible computational models of inference and learning with local update rules and the global gradient-based optimization of neural network models employed in machine learning. One particularly exciting connection is the correspondence between the locally informed optimization in predictive coding networks and the error backpropagation algorithm that is used to train state-of-the-art deep artificial neural networks. Here we focus on the related, but still largely under-explored connection between precision weighting in predictive coding networks and the Natural Gradient Descent algorithm for deep neural networks. Precision-weighted predictive coding is an interesting candidate for scaling up uncertainty-aware optimization -- particularly for models with large parameter spaces -- due to its distributed nature of the optimization process and the underlying local approximation of the Fisher information metric, the adaptive learning rate that is central to Natural Gradient Descent. Here, we show that hierarchical predictive coding networks with learnable precision indeed are able to solve various supervised and unsupervised learning tasks with performance comparable to global backpropagation with natural gradients and outperform their classical gradient descent counterpart on tasks where high amounts of noise are embedded in data or label inputs. When applied to unsupervised auto-encoding of image inputs, the deterministic network produces hierarchically organized and disentangled embeddings, hinting at the close connections between predictive coding and hierarchical variational inference.

Contributed chapter to "HANDBOOK OF RESEARCH ON ARTIFICIAL INTELLIGENCE, INNOVATION AND ENTREPRENEURSHIP" to be published by Edward Elgar Publishing Loizos Michael Abstract: This chapter discusses AI from the prism of an automated process for the organization of data, and exemplifies the role that explainability has to play in moving from the current generation of AI systems to the next one, where the role of humans is lifted from that of data annotators working for the AI systems to that of collaborators working with the AI systems. Keywords: data organization, automation, explainability, fourth AI revolution, learning, XIXO principle, machine coaching Acknowledgements: This work was supported by funding from the EU's Horizon 2020 Research and Innovation Programme under grant agreements no. Explainable automated organization of data. Although admittedly not a comprehensive definition of the wide scope of Artificial Intelligence (AI), this phrase does capture how AI has come to be ...

The second of two leaders from NYU's AI Now Institute, a small but influential organization researching the social implications of artificial intelligence, just joined the Biden administration to lay the groundwork for government AI policy. Their previous work suggests their presence might encourage the government to require new transparency from tech companies about how their algorithms work. The Federal Trade Commission earlier this month created an entirely new role for AI Now co-founder Meredith Whittaker, who will serve as senior adviser on AI for an agency where tech staff has been in flux despite a mission to get tougher on tech. AI Now alumna Rashida Richardson -- a law professor who served as director of policy research for the group and has a background studying the impact of AI systems like predictive policing tools -- joined the White House Office of Science and Technology Policy in July as senior policy adviser for data and democracy. "[Whittaker's] hiring is just the latest evidence of the FTC's attention on algorithms and algorithmic issues," said Laura Riposo VanDruff, former assistant director in the FTC's privacy and identity protection division and a partner at law firm Kelley Drye & Warren.

Apple is rumored to be developing several technologies outside of smartphones and tablets, such as a VR headset and a car, but new patents awarded to the tech giant on Thursday suggest it may be working on a drone. Approximately two patents describe small unmanned aerial vehicles (UAVs) that pair with wireless controllers or drones operated via an iPhone or a Nintendo DS. Apple, however, initially filed the patents in Singapore'to keep the projects a secret,' but have since filed the pair with the US Patent & Trademark Office. The images in the patents depict a small drone with four rotors, a common designed for small UAVs. Approximately two patents describe small unmanned aerial vehicles (UAVs) that pair with wireless controllers or drones operated via an iPhone or a Nintendo DS.

GDPR is one of the most comprehensive global data protection laws and it has claimed over $40 million in fines globally since its inception. Its success has encouraged the European Commission to introduce the idea of a similar regulation targeted towards Artificial Intelligence. This regulation will have implications for businesses that are both inside and outside of the EU that make AI available in the EU. At the time of this article, this is the first regulation targeted specifically towards AI and would have fees as large as €30 million or 6% of the company's total turnover, whichever is higher. You can find the full proposed regulation here, but we will highlight the main points.

Last year, Anthony Murphy, a visually impaired man who lives in Erie, Pennsylvania, visited the website of eyewear retailer Eyebobs using screen reader software. Its synthesized voice attempted to read out the page's content, as well as navigation buttons and menus. Eyebobs used artificial intelligence software from Israeli startup AccessiBe that promised to make its site easier for people with disabilities to use. But Murphy found it made it harder. AccessiBe says it can simplify the work of making websites accessible to people with impaired vision or other challenges by "replacing a costly, manual process with an automated, state-of-the-art AI technology."

Bipartisan legislation introduced on Thursday would direct the Defense Department to make libraries of its vast datasets publicly available and readily accessible via a strategic pilot program aimed at accelerating software and artificial intelligence capabilities development.

Monitoring of workers and setting performance targets through algorithms is damaging employees' mental health and needs to be controlled by new legislation, according to a group of MPs and peers. An "accountability for algorithms act'" would ensure that companies evaluate the effect of performance-driven regimes such as queue monitoring in supermarkets or deliveries-per-hour guidelines for delivery drivers, said the all-party parliamentary group (APPG) on the future of work. "Pervasive monitoring and target-setting technologies, in particular, are associated with pronounced negative impacts on mental and physical wellbeing as workers experience the extreme pressure of constant, real-time micro-management and automated assessment," said the APPG members in their report, the New Frontier: Artificial Intelligence at Work. The report recommends bringing in a new algorithms act, which it says would establish "a clear direction to ensure AI puts people first". It warns that "use of algorithmic surveillance, management and monitoring technologies that undertake new advisory functions, as well as traditional ones, has significantly increased during the pandemic".

What is AI bias and how can it affect human rights? In this blog post, we explore AI bias examples, challenges, and why the answer lies in ethical AI.