The MIT Technology Review highlights the alarming reality that technology often perpetuates racism, particularly through facial recognition systems biased against Black individuals. As noted by Charlton McIlwain, a media and communication professor at NYU, these algorithms are programmed with societal biases, endangering the Black and Latino communities. McIlwain states, “We’ve designed facial recognition technologies that target criminal suspects on the basis of skin color.” Such misuse of technology can significantly obstruct opportunities for marginalized groups.
As reported by McIlwain, the historical misuse of algorithms in political campaigning has contributed to racial profiling and mass surveillance in communities of color. This legacy has fostered systems that disproportionately target Black individuals, exacerbating existing societal inequities. McIlwain emphasizes that this creates a troubling foundation for systems that facilitate incarceration and surveillance of minorities.
The COVID-19 pandemic further illuminated these issues, with data showing higher infection rates among Black, Latino, and Indigenous people. Reports indicated that these communities were treated as threats rather than receiving support, mirroring disparaging rhetoric from political figures. McIlwain warns of the implications of technology used for controlling protests, threatening civil liberties in the name of public safety.
Artificial intelligence systems are still flawed due to human biases in their programming. A notable example includes a previous incident where Google’s algorithm misidentified images of Black individuals. Major companies like IBM and Amazon have recognized these issues; IBM’s executive director, Arvind Krishna, stated, “the fight against racism is as urgent as ever” in a Congress letter regarding facial recognition usage.
The performance disparity of these technologies underscores the need for accountability. MIT Media Lab’s findings reveal that 93% of Microsoft’s erroneous identifications impacted individuals with dark skin. Joy Buolamwini, co-author of the study, champions the need for responsible AI practices that respect human rights, particularly for marginalized groups.
Shifts in IT terminology reflect a broader movement toward inclusivity. Terms with connotations of discrimination are being reevaluated; for example, “master/slave” is moving to “leader/follower.” Linux and GitHub are on board with these changes, aiming to eliminate language that perpetuates systemic bias. This shift represents a commitment within tech to create inclusive tools that combat discrimination instead of sustaining it.
Technology can perpetuate racism, particularly through biased facial recognition algorithms targeting marginalized groups. The COVID-19 pandemic highlighted disproportionate impacts on Black and Latino communities. Major companies are re-evaluating AI usage and terminology, emphasizing a commitment to inclusivity and fair practices in tech. Accountability and reform are crucial for equitable technological advancements.
Technological advancements have, unfortunately, often reinforced racial biases, particularly through algorithms that misidentify and unjustly target marginalized communities. The COVID-19 crisis further highlighted these inequities, presenting urgent challenges that society must confront. Moving forward, the tech industry must prioritize inclusive language and responsible AI to promote equitable treatment for all individuals, addressing systemic inequities with urgency and diligence.
Original Source: www.techtarget.com
