People of color are adversely affected by artificial intelligence (AI) bias. The effects of AI bias have been noted in facial recognition technology, mortgage lending, and algorithms used to determine healthcare treatments. People impacted by AI bias are rarely represented in the development of AI technology (Atker et al., 2021). To prevent AI bias, including diverse perspectives in the creation of Responsible AI (RAI), artifacts that shape policies, procedures, and governance models, could address potential problems in the development of AI.

 RAI is an emerging business discipline that examines legal, ethical, and moral standpoints of technology development to help reduce AI bias (Barredo et al., 2020, Taylor et al., 2018). By tying impacted people to innovation and incorporating their ideas as stakeholders, innovations gain substantive and symbolic support from those who are most affected by innovation (Boon et al., 2021). My motivation is explore broader employee stakeholder participation in IS, AI and Organizational Learning. Therefore, I seek to answer the following research question: "How does the participation of African American employee stakeholders in the creation of Responsible AI "shaping artifacts" reduce bias in AI?"

• Let it RAIN for social Good. Mattias Brännström, Andreas Theodorou and Virginia Dignum.

• Accountability and Responsibility of Artificial Intelligence Decision-making Models in Indian Policy Landscape. Palak Malhotra and Amita Misra.

• Assessing Demographic Bias Transfer from Dataset to Model: A Case Study in Facial Expression Recognition. Iris Dominguez-Catena, Daniel Paternain and Mikel Galar.

>Debate Panel - Session Discussants: Elizabeth Adams, Gabriel Pedroza.

Three years later, how far have we gotten in putting the OECD AI principles into practice?

Governments and other stakeholders have been working to implement the OECD AI Principles to make artificial intelligence trustworthy for people and planet. This talk is a timely occasion to highlight work to date and discuss future priorities.

The talk will showcase recent initiatives developed by the OECD Working Party on Artificial Intelligence Governance (AIGO) and the OECD.AI Network of Experts, including the OECD.AI Policy Observatory, a catalogue of tools for trustworthy AI, a user-friendly framework for classifying different types of AI systems and a global AI incidents tracker. The discussion will seek to highlight good AI policy practices related to AI governance.

• A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems. Yi Qi, Philippa Ryan Conmy, Wei Huang, Xingyu Zhao and Xiaowei Huang.

• Increasingly Autonomous CPS: Taming Emerging Behaviors from an Architectural Perspective. Jerome Hugues and Daniela Cancila.

• CAISAR: A platform for Characterizing Artificial Intelligence Safety and Robustness. Julien Girard-Satabin, Michele Alberti, François Bobot, Zakaria Chihani andAugustin Lemesle.

>Debate Panel - Session Discussants: Luis Aranda, Douglas Lange, Mattias Brännström (Umeå University, Sweden)

Large pretrained language models like GPT-3 and PaLM  have generated enormous enthusiasm, and are capable of producing remarkably fluent language. But they have also been criticized on many grounds, and described as "stochastic parrots." Are they adequate as a basis for general intelligence, and if not, what would a better foundation for general intelligence look like?

• Revisiting the Evaluation of Deep Neural Networks for Pedestrian Detection. Patrick Feifel, Benedikt Franke, Arne Raulf, Friedhelm Schwenker, Frank Bonarens and Frank Köster.

• Improvement of Rejection for AI Safety through Loss-Based Monitoring. Daniel Scholz, Florian Hauer, Klaus Knobloch and Christian Mayr.

>Debate Panel - Session Discussants: Gary Marcus, John Burden, Gabriel Pedroza.

• Foundations of Trustworthy AI - The TAILOR project. Francesca Pratesi.

• Panel on Trustworthy AI - Session Discussants: Fosca Giannotti, Pilipp Slusallek, Giuseppe De Giacomo, Hector Geffner, and Holger Hoos.

We review several ways in which formal methods can enhance the quality of neural networks:
first, to learn neural networks with guaranteed properties;
second, to verify properties of neural networks;
and third, to enforce properties of neural networks at runtime.
For the first topic, we discuss reinforcement learning with temporal objectives in stochastic environments; for the second, decision procedures for reasoning about quantized neural networks; for the third, monitoring learned classifiers for novelty detection and fairness, and shielding learned controllers for safety and progress.

• Utilizing Class Separation Distance for the Evaluation of Corruption Robustness of Machine Learning Classifiers. Georg Siedel, Silvia Vock, Andrey Morozov and Stefan Voß.

• Safety-aware Active Learning with Perceptual Ambiguity and Criticality Assessment. Prajit T Rajendran, Guillaume Ollier, Huascar Espinoza, Morayo Adedjouma, Agnes Delaborde and Chokri Mraidha.

• Understanding Adversarial Examples Through Deep Neural Network's Classification Boundary and Uncertainty Regions. Juan Shu, Bowei Xi and Charles Kamhoua.

>Debate Panel - Session Discussants: Thomas A. Henzinger, Fernando Martinez Plumed.

Deep Learning (DL) has become a fundamental building block of learning-enabled autonomous systems. Notwithstanding its great potential, employing DL in safety- and security-critical applications, including robots providing service in healthcare facilities or drones used for inspection and maintenance, raises significant trustworthiness challenges. Within the European project SESAME, we develop a model-based approach supporting the systematic engineering of dependable learning-enabled robotic systems. In this talk, we will overview recent advances made by the project team to provide assurances for the trustworthy, robust and explainable operation of DL, focusing particularly on techniques for deep learning testing and uncertainty analysis.

• Leveraging generative models to characterize the failure conditions of image classifiers. Adrien Le Coz, Stéphane Herbin, and Faouzi Adjed.

• Feasibility of Inconspicuous GAN-generated Adversarial Patches against Object Detection. Svetlana Pavlitskaya, Bianca-Marina Codău and J. Marius Zöllner.

• Privacy Safe Representation Learning via Frequency Filtering Encoder. Jonghu Jeong, Minyong Cho, Philipp Benz, Jinwoo Hwang, Jeewook Kim, Seungkwan Lee and Tae-hoon Kim.

• Benchmarking and deeper analysis of adversarial patch attack on object detectors. Pol Labarbarie, Adrien Chan-Hon-Tong, Stéphane Herbin and Milad Leyli-Abadi.

>Debate Panel - Session Discussants: Simos Gerasimou, Bowei Xi (Purdue University), Gabriel Pedroza.

While still debating some questions (such as the likelihood of achieving Artificial General Intelligence), the AI community in particular and most of the related stakeholders in general, seem to be more or less convinced that "AI winters" are a thing of the past and that the current "summer" will never end. Indeed, this rapidly evolving field, especially through the recent Deep Learning advances, is too good at particular useful tasks to simply disappear. There is little doubt, for example, that neural networks are poised to permeate a growing number of everyday applications, including sensitive software where trust is paramount.

But as these artifacts move from a fad status to more stably ubiquitous components, their deepening interweaving with different aspects of society deserves a special attention to the development of methods and tools for an adequate characterization of AI trustworthiness. This colossal quest is made difficult by the intrinsic opacity of neural networks and their increasing size, making any method that can bring us closer to trustworthiness a precious commodity. In this talk, we take a retrospective look at some of these methods, and discuss their current added value to safety, as well as the promises they hold for the future of AI trustworthiness.

• The impact of averaging logits over probabilities on ensembles of neural networks. Cedrique Rovile Njieutcheu Tassi, Jakob Gawlikowski, Auliya Unnisa Fitri and Rudolph Triebel.

• Exploring Diversity in Neural Architectures for Safety. Michał Filipiuk, Vasu Singh.
• Constrained Policy Optimization for Controlled Contextual Bandit Exploration. Mohammad Kachuee, Sungjin Lee.
• A causal perspective on AI deception in games. Francis Rhys Ward, Francesca Toni and Francesco Belardinelli.

>Debate Panel - Session Discussants: Zakaria Chihani, Jose Hernandez-Orallo.