With Silicon Valley pushing the limits of technical innovation, its elite are talking a lot about the ethical implications of artificial intelligence (AI).
AI poses ethical quandaries that require careful thought, despite its potential to disrupt businesses and change society.
The moral imperative of tech titans
AI development is shaped in large part by the tech titans of Silicon Valley. Given their extensive resources and knowledge, it is morally required of them to guarantee that AI technologies are created and applied appropriately. This responsibility involves more than just following the law; it also entails actively addressing moral dilemmas and placing the welfare of society first.
The conflict between the two ex-cofounders of a nonprofit organisation that is now mocked as a “de-facto subsidiary” of Microsoft has been sparked by Elon Musk’s lawsuit against Sam Altman’s OpenAI.
The top names in Silicon Valley have also experienced a resurgence of their hero complex.
Musk filed a complaint on the 1st of March against OpenAI, claiming in part that the business has abandoned its original goal of developing artificial general intelligence (AGI) for the sake of humanity.
Attorneys for Musk contended that the company’s new board, which was established in November following an attempt to overthrow Altman as CEO, had turned to “refining an AGI to maximise profits” for its multi-billionaire investor, Microsoft, rather than just creating the technology.
The entire ordeal has given Silicon Valley’s more opinionated leaders the opportunity to pontificate about the future of AI, even though some doubts have been raised about how strong of a case Musk has.
Which way, Mr. AI?
Before introducing their technology to the public, leaders driving the deployment of AI had to address a crucial question: Open or closed?
The decision, which forms the core of Musk’s critique of OpenAI, is significant because it offers two radically dissimilar options for releasing AI.
The open option supports AI models that are open about how they are trained and collaboratively developed by a worldwide community of developers, at least in theory. Both the French company Mistral AI and Meta have committed to this strategy with Llama 2.
Although there are worries that malicious actors could misuse open source models, proponents highlight their many advantages to closed models like OpenAI’s GPT-4, which withholds the training data from the public.
And in fact, they’re growing quite righteous about it.
Part of this moralising emerged over the weekend when venture capitalist Marc Andreessen chose to address claims made by Vinod Khosla, who placed a $50 million wager on the business in 2019, regarding Musk’s legal battle with OpenAI.
By Khosla’s standards, national security considerations must be taken into account while evaluating OpenAI’s strategy. He said on X: “We are in a tech economic war with China and AI that is a must-win,” and then he asked Andreessen if he would consider The Manhattan Project to be open-source.
Andreessen took off with the comparison between the creation of AI now and the development of nuclear weapons in World War II.
He used it to justify why it shouldn’t be left to a small group of San Francisco residents to protect against, say, an espionage effort by the Chinese Communist Party if AI was truly as vital a technology to keep under wraps as military weaponry.
Andreessen stated on X that “what you’d expect to see is a rigorous security vetting and clearance process for everyone from the CEO to the cook, with monthly polygraphs and constant internal surveillance,” but he later clarified that this isn’t the case at OpenAI.
Andreessen conceded that the parallel was a little “absurd” given that AI uses arithmetic rather than nuclear weapons.
So what can be done?
Since AI systems mine data to make judgments or forecasts that affect specific people or groups, fairness in AI is a critical topic. Inadvertent or embedded in the data, bias has the power to reinforce societal biases, propagate discrimination, and worsen already-existing disparities. The creation of AI must take deliberate steps to remove prejudices and encourage equitable results in order to guarantee justice. To that end, here are a few strategies:
Diverse and representative data
The quality and diversity of data used to train AI algorithms are crucial. It is essential to ensure that the data sets used for AI development are representative of the population they are intended to serve. This includes diverse demographic groups and underrepresented communities to mitigate the risk of biased outcomes that disproportionately impact marginalised groups.
Robust data analysis
Rigorous data analysis is necessary to identify and address potential biases in AI algorithms. Regularly evaluating the data inputs and outputs can help uncover biases and take corrective actions. This process should involve multidisciplinary teams, including subject matter experts, ethicists, and diversity and inclusion specialists.
Transparent algorithms
In order to ensure accountability and remove potential bias, transparency in AI algorithms is essential. It is the goal of developers to make AI systems’ decision-making procedures comprehensible and explicable. This helps those who are impacted by AI to understand the decision-making process and contest decisions if needed.
Codes of conduct and ethical standards
For the development and application of AI, it is crucial to establish explicit ethical standards and codes of conduct. Fairness, moral judgement, and the proper application of AI technologies ought to be given top priority in these rules. They should also stress the importance of informed consent and privacy protection for individuals.
In conclusion, in the current era of AI, developing ethical AI is essential. We can use AI to improve lives without surrendering core values if we prioritise justice, correct biases, and encourage accountability. We can create a future where AI technologies serve justice, equality, and social well-being by using diverse and representative data, thorough analysis, transparency, ethical rules, audits, continuous monitoring, user feedback channels, and supportive legislative frameworks. Ethical AI has the potential to significantly improve the world if developers, corporations, governments, and individuals work together.
(Tashia Bernardus)