We present sustain.AI, an intelligent, context-aware recommender system that assists auditors and financial investors as well as the general public to efficiently analyze companies' sustainability reports. The tool leverages an end-to-end trainable architecture that couples a BERT-based encoding module with a multi-label classification head to match relevant text passages from sustainability reports to their respective law regulations from the Global Reporting Initiative (GRI) standards. We evaluate our model on two novel German sustainability reporting data sets and consistently achieve a significantly higher recommendation performance compared to multiple strong baselines. Furthermore, sustain.AI is publicly available Figure 1: A screenshot of the sustain.AI recommender tool.

Socially responsible investors build investment portfolios intending to incite social and environmental advancement alongside a financial return. Although Mean-Variance (MV) models successfully generate the highest possible return based on an investor's risk tolerance, MV models do not make provisions for additional constraints relevant to socially responsible (SR) investors. In response to this problem, the MV model must consider Environmental, Social, and Governance (ESG) scores in optimisation. Based on the prominent MV model, this study implements portfolio optimisation for socially responsible investors. The amended MV model allows SR investors to enter markets with competitive SR portfolios despite facing a trade-off between their investment Sharpe Ratio and the average ESG score of the portfolio.

Financial experts and analysts seek to predict the variability of financial markets. In particular, the correct prediction of this variability ensures investors successful investments. However, there has been a big trend in finance in the last years, which are the ESG criteria. Concretely, ESG (Economic, Social and Governance) criteria have become more significant in finance due to the growing importance of investments being socially responsible, and because of the financial impact companies suffer when not complying with them. Consequently, creating a stock portfolio should not only take into account its performance but compliance with ESG criteria. Hence, this paper combines mathematical modelling, with ESG and finance. In more detail, we use Bayesian optimization (BO), a sequential state-of-the-art design strategy to optimize black-boxes with unknown analytical and costly-to compute expressions, to maximize the performance of a stock portfolio under the presence of ESG criteria soft constraints incorporated to the objective function. In an illustrative experiment, we use the Sharpe ratio, that takes into consideration the portfolio returns and its variance, in other words, it balances the trade-off between maximizing returns and minimizing risks. In the present work, ESG criteria have been divided into fourteen independent categories used in a linear combination to estimate a firm total ESG score. Most importantly, our presented approach would scale to alternative black-box methods of estimating the performance and ESG compliance of the stock portfolio. In particular, this research has opened the door to many new research lines, as it has proved that a portfolio can be optimized using a BO that takes into consideration financial performance and the accomplishment of ESG criteria.

The organizational use of artificial intelligence (AI) has rapidly spread across various sectors. Alongside the awareness of the benefits brought by AI, there is a growing consensus on the necessity of tackling the risks and potential harms, such as bias and discrimination, brought about by advanced AI technologies. A multitude of AI ethics principles have been proposed to tackle these risks, but the outlines of organizational processes and practices for ensuring socially responsible AI development are in a nascent state. To address the paucity of comprehensive governance models, we present an AI governance framework, the hourglass model of organizational AI governance, which targets organizations that develop and use AI systems. The framework is designed to help organizations deploying AI systems translate ethical AI principles into practice and align their AI systems and processes with the forthcoming European AI Act. The hourglass framework includes governance requirements at the environmental, organizational, and AI system levels. At the AI system level, we connect governance requirements to AI system life cycles to ensure governance throughout the system's life span. The governance model highlights the systemic nature of AI governance and opens new research avenues into its practical implementation, the mechanisms that connect different AI governance layers, and the dynamics between the AI governance actors. The model also offers a starting point for organizational decision-makers to consider the governance components needed to ensure social acceptability, mitigate risks, and realize the potential of AI.

Alternative credit options can mean the difference between financial well-being and financial hardship for many borrowers. Fintech advancements such as buy-now-pay-later, plus the combination of credit models driven by artificial intelligence and machine learning, may pave the way for a fairer and more inclusive future of credit. But lessons from the financial crisis ring clear: When only one part of the market is required to comply with regulations, the other will compete by offering disadvantageous and risky products. Regulators are now faced with how to advance a regulatory framework that encourages innovation while protecting consumers. Buy now/pay later options spurred marked industry growth, as well as artificial intelligence and machine learning advances during the pandemic, with implications and improved assistance for underserved communities.

Google.org connects nonprofits to funding & additional resources. Learn about our philanthropy program & goal to aid underserved communities.

Environmental, social and governance (ESG) engagement of companies moved into the focus of public attention over recent years. With the requirements of compulsory reporting being implemented and investors incorporating sustainability in their investment decisions, the demand for transparent and reliable ESG ratings is increasing. However, automatic approaches for forecasting ESG ratings have been quite scarce despite the increasing importance of the topic. In this paper, we build a model to predict ESG ratings from news articles using the combination of multivariate timeseries construction and deep learning techniques. A news dataset for about 3,000 US companies together with their ratings is also created and released for training. Through the experimental evaluation we find out that our approach provides accurate results outperforming the state-of-the-art, and can be used in practice to support a manual determination or analysis of ESG ratings.

MIT Sloan Management Review and Boston Consulting Group (BCG) have assembled an international panel of AI experts that includes academics and practitioners to help us gain insights into how responsible artificial intelligence (RAI) is being implemented in organizations worldwide. This month's question for our panelists: Should an organization tie its RAI efforts to its overall corporate social responsibility (CSR) efforts? The results present a mixed picture. While 52% of panelists (11 out of 21) believe that an organization's RAI and CSR efforts should be linked, 24% do not (5 out of 21 disagree or strongly disagree), and an equal percentage expressed ambivalence (5 out of 21 neither agree nor disagree). Despite the lack of consensus, there are some common characteristics among those who agree that organizations should link their RAI and CSR efforts, as well as some concerns shared among the remaining panelists.

Dr. Isabel Millar is a philosopher and cultural theorist from London. She received her PhD from Kingston University, School of Art in 2021. She holds an MA in Psychosocial Studies from Birkbeck College, University of London and a BA in Philosophy from The University of Sussex. She writes and talks about AI, sex, the body, space, culture, film and the future. Isabel is also a Research Fellow at the Centre for Critical Thought, University of Kent and Research Fellow and faculty at the Global Centre for Advanced Studies, where she teaches with GCAS' newly formed Institute of Psychoanalysis.

Companies are under mounting pressure from regulators, investors, and consumers to progress toward more sustainable and socially responsible business operations -- and to demonstrate these measures in a robust and verifiable way. In fact, corporate responsibility and environmental sustainability risks tied as the third highest concerns for organizations, as ranked by large corporations in a 2021 Forrester report. However, the various types of data that companies need to understand and report on sustainability initiatives remains highly fragmented and difficult for all relevant parties to access. To help organizations respond to these challenges, IBM has acquired Envizi, a leading data and analytics software provider for environmental performance management. Envizi complements IBM's growing portfolio of AI-powered software -- including IBM Maximo asset management solutions, IBM Sterling supply chain solutions and IBM Environmental Intelligence Suite -- to help companies assess the impacts of the environment on business and of business on the environment.