Text-to-image (T2I) generative models are largely used in AI-powered real-world applications and value creation. However, their strategic deployment raises critical concerns for responsible AI management, particularly regarding the reproduction and amplification of race- and gender-related stereotypes that can undermine organizational ethics. In this work, we investigate whether such societal biases are systematically encoded within the pretrained latent spaces of state-of-the-art T2I models. We conduct an empirical study across the five most popular open-source models, using ten neutral, profession-related prompts to generate 100 images per profession, resulting in a dataset of 5,000 images evaluated by diverse human assessors representing different races and genders. We demonstrate that all five models encode and amplify pronounced societal skew: caregiving and nursing roles are consistently feminized, while high-status professions such as corporate CEO, politician, doctor, and lawyer are overwhelmingly represented by males and mostly White individuals. We further identify model-specific patterns, such as QWEN-Image's near-exclusive focus on East Asian outputs, Kandinsky's dominance of White individuals, and SDXL's comparatively broader but still biased distributions. These results provide critical insights for AI project managers and practitioners, enabling them to select equitable AI models and customized prompts that generate images in alignment with the principles of responsible AI. We conclude by discussing the risks of these biases and proposing actionable strategies for bias mitigation in building responsible GenAI systems. The code and Data Repository: https://github.com/Sufianlab/T2IBias

To translate the Standard Bank Group (SBG) data vision and strategy into applicable data strategies in Standard Bank Offshore to support SBG objectives. To oversee the implementation of the data strategy by co-ordinating and facilitating data programmes to enable data driven business decisions that are consistent and effective. To enforce governance and compliance ensuring alignment to SBG framework, policies, and standards. To Provide the business leadership role that has the primary enterprise accountability for value creation by means of the organization's data and analytics assets, as well as the data and analytics ecosystem. To define, develop, and execute the data monetisation strategy by providing guidance, input and leadership across the data, analytics using AI, ML, and advanced data science methodologies.

Innovation and the Pandemic Propelled Performance The 2022 TMT Value Creators Report February 28, 2022 By Simon Bamberger, Hady Farag, Derek Kennedy, Franck Luisada, Michaela Novakov, Vaishali Rastogi, and Neal Zuckerman The outsized role that technology, media, and telecommunications (TMT) companies play in modern life has made the sector a leader in creating shareholder value. From 2016 to 2021, TMT companies collectively outperformed those in many other industries in total shareholder return (TSR), according to BCG's 2022 Value Creators Report. Among our findings: The lion's share of TMT value creation came from tech players, which from 2016 to 2021 had a median annual TSR performance of 30%, more than double the median overall return of 13% for the 33 industries we studied. Of the 232 TMT companies in our sample, 70% posted a higher TSR during the period that included the peak of the pandemic, the 21 months from March 2020 to November 2021, than during the prior 21 months. Continuing on the same growth trajectory may be a challenge given recent investor anxieties about inflation, monetary policy, and moderating earnings growth.

Today we see clear movement and momentum to decarbonization and the green energy transition. In parallel, the rise in digital technology and advanced analytics provide unique opportunities to not only migrate to new energy technologies, but to monitor progress, predict performance, integrate systems, ensure reliability and resiliency – and improve sustainability by optimizing products, solutions, and services like never before. At the same time, we have changing dynamics in the sector that increase its complexity. Grids are moving from centralized to decentralized models. Energy producers have multi-OEM (original equipment manufacturer) solutions that must be monitored as a system to ensure uptime and output.

In Part 1 of the series "Leveraging Agility to Create Economies of Learning Mindset", I discussed the precarious nature of the CDO role given the expectations of building the organization's data and analytic capabilities while simultaneously delivering short-term business impact. CDO Data-to-Business Innovation Dilemma: Deliver meaningful and relevant business outcomes in the short-term while simultaneously and continuously building and transforming the organization's data and analytics assets and capabilities. The key to addressing the CDO Data-to-Business Innovation Dilemma is to view the development of the organization's data and analytics plan as a journey, not an event. But the data and analytics journey is fraught with unknown challenges and new technology and business developments that only surface as the data science and business teams move along the data and analytics journey. Like the movie "Jason and the Argonauts", your data and analytics journey must be prepared for whatever is thrown at them (like that darn skeleton army), and then pivot and adjust accordingly (Figure 1).

OpenAI's DALL-E produced this image when prompted with the title of this article ("The Biggest ... [ ] Opportunity In Generative AI Is Language, Not Images"). The buzz around generative AI today is deafening. Generative AI refers to artificial intelligence that can generate novel content, rather than simply analyzing or acting on existing data. No topic in the world of technology is attracting more attention and hype right now. The white-hot epicenter of today's generative AI craze has been text-to-image AI. Text-to-image AI models generate detailed original images based on simple written inputs. The most well-known of these models include Stable Diffusion, Midjourney and OpenAI's DALL-E.

Anno 2021, companies in retail have more and more data at their disposal. Data about their business processes, customers, products; virtually every aspect of modern business is subject to large amounts of back-end data. This data can then be used to make rational and informed decisions and strategic decisions. Furthermore, this data can be used, for example, to optimize the sales process or to provide customers with better service. In short, data is invaluable to companies that want to move with a digital paradigm that is constantly shifting.

Soumen helps enterprises to embrace data and augment cognitive intelligence. Every day we read about some new AI breakthrough. AI is omnipresent in our everyday lives whether we use mobile devices, wearables or voice assistants or stream our favorite shows. Enterprises control the epicenter of the AI economy, and they drive the innovation through a new trajectory, attracting investors through their impeccably timed innovations promising the new efficiency frontier. A new forecast from IDC Worldwide predicts the AI market will have worldwide revenues surpassing $300 billion in 2024 with a five-year compound annual growth rate (CAGR) of 17.1%.

Defining the appropriate organizational design with which to embed AI and data-driven decision-making across an organization is complex. Multiple challenges must be overcome to align AI with all parts of the organization, from engineering to customer-facing units, and to upskill the workforce effectively. Success – even at the level of specific AI project implementations – is not a given: executives need to understand new project execution risk factors (beyond usual ones such as change management challenges) that can lead to costly project failures. These may include very challenging data issues or difficulties from continuous risk management of the AI models deployed. This only covers half of the AI map for executives.

This is a guest post by Kirk Borne, Ph.D., Chief Science Officer at DataPrime.ai, Kirk is also a consultant, astrophysicist, data scientist, blogger, data literacy advocate and renowned speaker, and is one of the most recognized names in the industry. A survey of 1,100 data practitioners and business leaders reported that 84% of organizations consider data literacy to be a core business skill, agreeing with the statement that the inability of the workforce to use and analyze data effectively can hamper their business success. In addition, 36% said data literacy is crucial to future-proofing their business. Another survey found that 75% of employees are not comfortable using data.