The world is awash with buzzwords like AI, machine learning, and big data. With the release of ChatGPT and countless other consumer-facing AI platforms, misconceptions around AI and tech jargon have never been greater or more troubling. Misunderstanding AI can exacerbate fear and misinformation, fuel poorly devised regulations, spur unfounded job loss anxieties, erode trust in institutions employing AI, and widen the digital divide. Thus, AI literacy is critical to establish appropriate rules and regulations surrounding AI through the democratic process. This article seeks to clarify the jargon and explain AI-bias and why it is important to be aware of its risks.
AI literacy means having the skills and competencies for using AI technologies and applications as tools, viewing them critically, understanding their context and embedded principles, and questioning their design and implementation. While approximately 58% of the U.S. adult population has heard of ChatGPT only about 14% have interacted with the service. Embedding AI literacy in K-12, higher, and workforce education can foster a workforce that uses AI to heighten their abilities instead of being threatened by AI’s capabilities.
Big data is often described in terms of the ‘three Vs’: high-volume (massive datasets), high-velocity (real-time data), and high-variety (different sources and forms) information. The Information Commissioner’s Office (ICO) defines big data as data that, due to several varying characteristics, is difficult to analyze using traditional data analysis methods.
AI is developed as a response to make sense of all this data. AI programs analyze this data by learning from the data to respond intelligently to new data and adapt their outputs accordingly, thus giving computers intelligent thought.
Under the umbrella of AI, machine learning comes in. Machine learning is one of the most advanced and fastest-growing approaches to AI, referring to the set of techniques and tools that allow computers to ‘think’ by creating mathematical algorithms based on accumulated data.
However, the normalization of AI use also comes with implications for data protection and privacy. Machine learning can broadly be categorized into supervised and unsupervised learning. In supervised learning, algorithms are trained on labeled datasets to create world models for future predictions. In contrast, unsupervised learning algorithms find patterns in the data without any specific instructions, leading to the potential of the “Black Box Effect”, where the opacity of the program makes it difficult to understand the reasons for decisions made as a result of deep learning which can result in AI bias. AI bias denotes any way that AI and data analytics tools can perpetuate or amplify bias. The most common form of bias in AI comes from the historical data used to train the algorithms.
This phenomenon can have drastic real-world consequences, notably in health care technology where algorithms have been found to reflect social inequities but also exacerbate them in a field that is already particularly susceptible to racial bias. AI use in hiring technology is also growing with the popularity of automated hiring platforms like HireVue. It is essential that we understand how these algorithms learn, and develop them with broader, more diverse teams with as many eyes on the process.
Promoting AI literacy is not just beneficial — it’s essential. It equips society to understand and shape the AI landscape, maximizing benefits and minimizing risks. As AI increasingly permeates our lives understanding — we must eliminate the knowledge gap to responsibly interact with AI, armed with knowledge.
Ben Stevens, Intern
The European Parliament has passed a draft law, The AI Act, aimed at regulating high-risk AI applications, placing restrictions on facial recognition software, requiring AI system creators to disclose data usage, and insisting on the publication of copyrighted material summaries used in training generative AI systems like ChatGPT. A final version of the law is expected by the end of 2023. Some have expressed concerns about the potential impediments on innovation and compliance issues this law could cause. The requires pre-use risk assessments to identify and mitigate any potential risks that could arise from the use of these AI technologies like bias, privacy, security, reliability. One significant debate centers on the ban of live facial recognition and biometric data scraping from social media. The final version will be jointly agreed upon by the European Parliament, European Commission, and the Council of the European Union.
Humanly.io is an A-powered virtual recruiter and interview assistant putting equity in the hiring process at the core of their mission to create more equitable, efficient and empathetic conversations with job candidates and employees. The company solves retention problems for companies like Microsoft, Airbnb, Amazon, Ascension Healthcare and others, and received a $4.2M seed fund raise from Zeal Capital Partners the successful Venture Capital investor in the DEI space. Humanly.io uses linguistic theory and a primary focus on DEIB (diversity, equity, inclusion, and belonging) in hiring to help talent acquisition teams engage and hire candidates address inherent biases in human-led recruiting processes.
Sir Paul McCartney says artificial intelligence has enabled him to create a final Beatles song that will be released later in the year by creating a digital version of John Lenon’s voice. The idea to use AI to reconstruct John Lenon’s voice came from Peter Jackson’s 2021 documentary series on the Beatles, Get Back. In the documentary, the dialogue editor Emile de la Rey used custom AI program to recognize the Beatles’ voices to separate them from background noise. The same process allowed McCartney to sing with Lenon’s AI-generated voice at the Glastonbury music festival last year.
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