Tech Brands Advance Online Equity with Inclusive Initiatives

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Casual Conversations

Brands and businesses need to ensure the online space is accessible and beneficial for all consumers – an issue we interrogate in our Techquity report. With awareness of this issue surging, we spotlight brands and platforms making positive steps towards digital equity.

Community-Centric Computing: To provide local consumers with internet connection and coding curriculums, the University of South Carolina and Benedict College are collaborating with Apple to launch eight computer labs state-wide. While located in proximity to schools and universities, the labs will be open to the local community free of charge, to encourage digital literacy. This initiative will appeal to the 59% of US parents that say their children face digital obstacles to learning (Pew, 2020); tech brands would be wise to emulate Apple and provide digital services to under-connected consumers.

Playing for a Better Future: In March, the US-based Entertainment Software Association (ESA) announced a $1m investment initiative supporting to work of Black Girls Code, a not-for-profit organisation that encourages tech education for Black women. The investment is supplemented by workshops and mentoring by industry leaders. Also see Women in Gaming.

Accessible Education Initiatives: In February, Microsoft launched a call-out for AI-enabled education innovations that empower people with disabilities (PWD), as part of the company’s AI for Accessibility Program. Established brands should leverage their market power to accelerate start-ups that are creating accessibility-centric initiatives.

Busting Algorithmic Bias: Facebook aims to help identify and eradicate digital bias with Casual Conversations, a new dataset of over 45,000 videos featuring over 3,000 participants, launched in early April. The publicly available dataset features people with a range of skin tones and visual features stating their age and gender to help refine facial recognition AI systems. Facebook intends the dataset to be used to flag potential bias and ensure fairness in computer vision and audio models.