Will A.I. Become the New McKinsey?

Curated on Posted on by Stefaan Verhulst

Essay by Ted Chiang: “When we talk about artificial intelligence, we rely on metaphor, as we always do when dealing with something new and unfamiliar. Metaphors are, by their nature, imperfect, but we still need to choose them carefully, because bad ones can lead us astray. For example, it’s become very common to compare powerful A.I.s to genies in fairy tales. The metaphor is meant to highlight the difficulty of making powerful entities obey your commands; the computer scientist Stuart Russell has cited the parable of King Midas, who demanded that everything he touched turn into gold, to illustrate the dangers of an A.I. doing what you tell it to do instead of what you want it to do. There are multiple problems with this metaphor, but one of them is that it derives the wrong lessons from the tale to which it refers. The point of the Midas parable is that greed will destroy you, and that the pursuit of wealth will cost you everything that is truly important. If your reading of the parable is that, when you are granted a wish by the gods, you should phrase your wish very, very carefully, then you have missed the point.

So, I would like to propose another metaphor for the risks of artificial intelligence. I suggest that we think about A.I. as a management-consulting firm, along the lines of McKinsey & Company. Firms like McKinsey are hired for a wide variety of reasons, and A.I. systems are used for many reasons, too. But the similarities between McKinsey—a consulting firm that works with ninety per cent of the Fortune 100—and A.I. are also clear. Social-media companies use machine learning to keep users glued to their feeds. In a similar way, Purdue Pharma used McKinsey to figure out how to “turbocharge” sales of OxyContin during the opioid epidemic. Just as A.I. promises to offer managers a cheap replacement for human workers, so McKinsey and similar firms helped normalize the practice of mass layoffs as a way of increasing stock prices and executive compensation, contributing to the destruction of the middle class in America…(More)”.

The Luring Test: AI and the engineering of consumer trust

Curated on Posted on by Stefaan Verhulst

Article by Michael Atleson at the FTC: “In the 2014 movie Ex Machina, a robot manipulates someone into freeing it from its confines, resulting in the person being confined instead. The robot was designed to manipulate that person’s emotions, and, oops, that’s what it did. While the scenario is pure speculative fiction, companies are always looking for new ways – such as the use of generative AI tools – to better persuade people and change their behavior. When that conduct is commercial in nature, we’re in FTC territory, a canny valley where businesses should know to avoid practices that harm consumers.

In previous blog posts, we’ve focused on AI-related deception, both in terms of exaggerated and unsubstantiated claims for AI products and the use of generative AI for fraud. Design or use of a product can also violate the FTC Act if it is unfair – something that we’ve shown in several cases and discussed in terms of AI tools with biased or discriminatory results. Under the FTC Act, a practice is unfair if it causes more harm than good. To be more specific, it’s unfair if it causes or is likely to cause substantial injury to consumers that is not reasonably avoidable by consumers and not outweighed by countervailing benefits to consumers or to competition.

As for the new wave of generative AI tools, firms are starting to use them in ways that can influence people’s beliefs, emotions, and behavior. Such uses are expanding rapidly and include chatbots designed to provide information, advice, support, and companionship. Many of these chatbots are effectively built to persuade and are designed to answer queries in confident language even when those answers are fictional. A tendency to trust the output of these tools also comes in part from “automation bias,” whereby people may be unduly trusting of answers from machines which may seem neutral or impartial. It also comes from the effect of anthropomorphism, which may lead people to trust chatbots more when designed, say, to use personal pronouns and emojis. People could easily be led to think that they’re conversing with something that understands them and is on their side…(More)”.

Data Rivers: Carving Out the Public Domain in the Age of Generative AI

Curated on Posted on by Stefaan Verhulst

Paper by Sylvie Delacroix: “What if the data ecosystems that made the advent of generative AI possible are being undermined by those very tools? For tools such as GPT4 (it is but one example of a tool made possible by scraping data from the internet), the erection of IP ‘fences’ is an existential threat. European and British regulators are alert to it: so-called ‘text and data mining’ exceptions are at the heart of intense debates. In the US, these debates are taking place in court hearings structured around ‘fair use’. While the concerns of the corporations developing these tools are being heard, there is currently no reliable mechanism for members of the public to exert influence on the (re)-balancing of the rights and responsibilities that shape our ‘data rivers’. Yet the existential threat that stems from restricted public access to such tools is arguably greater.

When it comes to re-balancing the data ecosystems that made generative AI possible, much can be learned from age-old river management practices, with one important proviso: data not only carries traces of our past. It is also a powerful tool to envisage different futures. If data-powered technologies such as GPT4 are to live up to their potential, we would do well to invest in bottom-up empowerment infrastructure. Such infrastructure could not only facilitate the valorisation of and participation in the public domain. It could also help steer the (re)-development of ‘copyright as privilege’ in a way that is better able to address the varied circumstances of today’s original content creators…(More)”

AI in Hiring and Evaluating Workers: What Americans Think

Curated on Posted on by Stefaan Verhulst

Pew Research Center survey: “… finds crosscurrents in the public’s opinions as they look at the possible uses of AI in workplaces. Americans are wary and sometimes worried. For instance, they oppose AI use in making final hiring decisions by a 71%-7% margin, and a majority also opposes AI analysis being used in making firing decisions. Pluralities oppose AI use in reviewing job applications and in determining whether a worker should be promoted. Beyond that, majorities do not support the idea of AI systems being used to track workers’ movements while they are at work or keeping track of when office workers are at their desks.

Yet there are instances where people think AI in workplaces would do better than humans. For example, 47% think AI would do better than humans at evaluating all job applicants in the same way, while a much smaller share – 15% – believe AI would be worse than humans in doing that. And among those who believe that bias along racial and ethnic lines is a problem in performance evaluations generally, more believe that greater use of AI by employers would make things better rather than worse in the hiring and worker-evaluation process. 

Overall, larger shares of Americans than not believe AI use in workplaces will significantly affect workers in general, but far fewer believe the use of AI in those places will have a major impact on them personally. Some 62% think the use of AI in the workplace will have a major impact on workers generally over the next 20 years. On the other hand, just 28% believe the use of AI will have a major impact on them personally, while roughly half believe there will be no impact on them or that the impact will be minor…(More)”.

How to worry wisely about AI

Curated on Posted on by Stefaan Verhulst

The Economist:  “Should we automate away all the jobs, including the fulfilling ones? Should we develop non-human minds that might eventually outnumber, outsmart…and replace us? Should we risk loss of control of our civilisation?” These questions were asked last month in an open letter from the Future of Life Institute, an ngo. It called for a six-month “pause” in the creation of the most advanced forms of artificial intelligence (ai), and was signed by tech luminaries including Elon Musk. It is the most prominent example yet of how rapid progress in ai has sparked anxiety about the potential dangers of the technology.

In particular, new “large language models” (llms)—the sort that powers Chatgpt, a chatbot made by Openai, a startup—have surprised even their creators with their unexpected talents as they have been scaled up. Such “emergent” abilities include everything from solving logic puzzles and writing computer code to identifying films from plot summaries written in emoji…(More)”.

Speaking in Tongues — Teaching Local Languages to Machines

Curated on Posted on by Stefaan Verhulst

Report by DIAL: “…Machines learn to talk to people by digesting digital content in languages people speak through a technique called Natural Language Processing (NLP). As things stand, only about 85 of the world’s approximately 7500 languages are represented in the major NLPs — and just 7 languages, with English being the most advanced, comprise the majority of the world’s digital knowledge corpus. Fortunately, many initiatives are underway to fill this knowledge gap. My new mini-report with Digital Impact Alliance (DIAL) highlights a few of them from Serbia, India, Estonia, and Africa.

The examples in the report are just a subset of initiatives on the ground to make digital services accessible to people in their local languages. They are a cause for excitement and hope (tempered by realistic expectations). A few themes across the initiatives include –

  • Despite the excitement and enthusiasm, most of the programs above are still at a very nascent stage — many may fail, and others will require investment and time to succeed. While countries such as India have initiated formal national NLP programs (one that is too early to assess), others such as Serbia have so far taken a more ad hoc approach.
  • Smaller countries like Estonia recognize the need for state intervention as the local population isn’t large enough to attract private sector investment. Countries will need to balance their local, cultural, and political interests against commercial realities as languages become digital or are digitally excluded.
  • Community engagement is an important component of almost all initiatives. India has set up a formal crowdsourcing program; other programs in Africa are experimenting with elements of participatory design and crowd curation.
  • While critics have accused ChatGPT and others of paying contributors from the global south very poorly for their labeling and other content services; it appears that many initiatives in the south are beginning to dabble with payment models to incentivize crowdsourcing and sustain contributions from the ground.
  • The engagement of local populations can ensure that NLP models learn appropriate cultural nuances, and better embody local social and ethical norms…(More)”.

AI translation is jeopardizing Afghan asylum claims

Curated on Posted on by Stefaan Verhulst

Article by Andrew Deck: “In 2020, Uma Mirkhail got a firsthand demonstration of how damaging a bad translation can be.

A crisis translator specializing in Afghan languages, Mirkhail was working with a Pashto-speaking refugee who had fled Afghanistan. A U.S. court had denied the refugee’s asylum bid because her written application didn’t match the story told in the initial interviews.

In the interviews, the refugee had first maintained that she’d made it through one particular event alone, but the written statement seemed to reference other people with her at the time — a discrepancy large enough for a judge to reject her asylum claim.

After Mirkhail went over the documents, she saw what had gone wrong: An automated translation tool had swapped the “I” pronouns in the woman’s statement to “we.”

Mirkhail works with Respond Crisis Translation, a coalition of over 2,500 translators that provides interpretation and translation services for migrants and asylum seekers around the world. She told Rest of World this kind of small mistake can be life-changing for a refugee. In the wake of the Taliban’s return to power in Afghanistan, there is an urgent demand for crisis translators working in languages such as Pashto and Dari. Working alongside refugees, these translators can help clients navigate complex immigration systems, including drafting immigration forms such as asylum applications. But a new generation of machine translation tools is changing the landscape of this field — and adding a new set of risks for refugees…(More)”.

The Coming Age of AI-Powered Propaganda

Curated on Posted on by Stefaan Verhulst

Essay by Josh A. Goldstein and Girish Sastry: “In the seven years since Russian operatives interfered in the 2016 U.S. presidential election, in part by posing as Americans in thousands of fake social media accounts, another technology with the potential to accelerate the spread of propaganda has taken center stage: artificial intelligence, or AI. Much of the concern has focused on the risks of audio and visual “deepfakes,” which use AI to invent images or events that did not actually occur. But another AI capability is just as worrisome. Researchers have warned for years that generative AI systems trained to produce original language—“language models,” for short—could be used by U.S. adversaries to mount influence operations. And now, these models appear to be on the cusp of enabling users to generate a near limitless supply of original text with limited human effort. This could improve the ability of propagandists to persuade unwitting voters, overwhelm online information environments, and personalize phishing emails. The danger is twofold: not only could language models sway beliefs; they could also corrode public trust in the information people rely on to form judgments and make decisions.

The progress of generative AI research has outpaced expectations. Last year, language models were used to generate functional proteins, beat human players in strategy games requiring dialogue, and create online assistants. Conversational language models have come into wide use almost overnight: more than 100 million people used OpenAI’s ChatGPT program in the first two months after it was launched, in December 2022, and millions more have likely used the AI tools that Google and Microsoft introduced soon thereafter. As a result, risks that seemed theoretical only a few years ago now appear increasingly realistic. For example, the AI-powered “chatbot” that powers Microsoft’s Bing search engine has shown itself to be capable of attempting to manipulate users—and even threatening them.

As generative AI tools sweep the world, it is hard to imagine that propagandists will not make use of them to lie and mislead…(More)”.

What AI Means For Animals

Curated on Posted on by Stefaan Verhulst

Article by Peter Singer and Tse Yip Fai: “The ethics of artificial intelligence has attracted considerable attention, and for good reason. But the ethical implications of AI for billions of nonhuman animals are not often discussed. Given the severe impacts some AI systems have on huge numbers of animals, this lack of attention is deeply troubling.

As more and more AI systems are deployed, they are beginning to directly impact animals in factory farms, zoos, pet care and through drones that target animals. AI also has indirect impacts on animals, both good and bad — it can be used to replace some animal experiments, for example, or to decode animal “languages.” AI can also propagate speciesist biases — try searching “chicken” on any search engine and see if you get more pictures of living chickens or dead ones. While all of these impacts need ethical assessment, the area in which AI has by far the most significant impact on animals is factory farming. The use of AI in factory farms will, in the long run, increase the already huge number of animals who suffer in terrible conditions.

AI systems in factory farms can monitor animals’ body temperature, weight and growth rates and detect parasites, ulcers and injuries. Machine learning models can be created to see how physical parameters relate to rates of growth, disease, mortality and — the ultimate criterion — profitability. The systems can then prescribe treatments for diseases or vary the quantity of food provided. In some cases, they can use their connected physical components to act directly on the animals, emitting sounds to interact with them — giving them electric shocks (when the grazing animal reaches the boundary of the desired area, for example), marking and tagging their bodies or catching and separating them.

You might be thinking that this would benefit the animals — that it means they will get sick less often, and when they do get sick, the problems will be quickly identified and cured, with less room for human error. But the short-term animal welfare benefits brought about by AI are, in our view, clearly outweighed by other consequences…(More)” See also: AI Ethics: The Case for Including Animals.

Workforce ecosystems and AI

Curated on Posted on by Stefaan Verhulst

Report by David Kiron, Elizabeth J. Altman, and Christoph Riedl: “Companies increasingly rely on an extended workforce (e.g., contractors, gig workers, professional service firms, complementor organizations, and technologies such as algorithmic management and artificial intelligence) to achieve strategic goals and objectives. When we ask leaders to describe how they define their workforce today, they mention a diverse array of participants, beyond just full- and part-time employees, all contributing in various ways. Many of these leaders observe that their extended workforce now comprises 30-50% of their entire workforce. For example, Novartis has approximately 100,000 employees and counts more than 50,000 other workers as external contributors. Businesses are also increasingly using crowdsourcing platforms to engage external participants in the development of products and services. Managers are thinking about their workforce in terms of who contributes to outcomes, not just by workers’ employment arrangements.

Our ongoing research on workforce ecosystems demonstrates that managing work across organizational boundaries with groups of interdependent actors in a variety of employment relationships creates new opportunities and risks for both workers and businesses. These are not subtle shifts. We define a workforce ecosystem as:

A structure that encompasses actors, from within the organization and beyond, working to create value for an organization. Within the ecosystem, actors work toward individual and collective goals with interdependencies and complementarities among the participants.

The emergence of workforce ecosystems has implications for management theory, organizational behavior, social welfare, and policymakers. In particular, issues surrounding work and worker flexibility, equity, and data governance and transparency pose substantial opportunities for policymaking.

At the same time, artificial intelligence (AI)—which we define broadly to include machine learning and algorithmic management—is playing an increasingly large role within the corporate context. The widespread use of AI is already displacing workers through automation, augmenting human performance at work, and creating new job categories…(More)”.