Politics and technology

False news stories, images, video and audio could be tailored to audiences and created at scale by next spring

Sat 20 May 2023 06.00 EDT

Next years elections in Britain and the US could be marked by a wave of AI-powered disinformation, experts have warned, as generated images, text and deepfake videos go viral at the behest of swarms of AI-powered propaganda bots.

Sam Altman, CEO of the ChatGPT creator, OpenAI, told a congressional hearing in Washington this week that the models behind the latest generation of AI technology could manipulate users.

The general ability of these models to manipulate and persuade, to provide one-on-one interactive disinformation is a significant area of concern, he said.

Regulation would be quite wise: people need to know if theyre talking to an AI, or if content that theyre looking at is generated or not. The ability to really model to predict humans, I think is going to require a combination of companies doing the right thing, regulation and public education.

The prime minister, Rishi Sunak, said on Thursday the UK would lead on limiting the dangers of AI. Concerns over the technology have soared after breakthroughs in generative AI, where tools like ChatGPT and Midjourney produce convincing text, images and even voice on command.

Where earlier waves of propaganda bots relied on simple pre-written messages sent en masse, or buildings full of paid trolls to perform the manual work of engaging with other humans, ChatGPT and other technologies raise the prospect of interactive election interference at scale.

An AI trained to repeat talking points about Taiwan, climate breakdown or LGBT+ rights could tie up political opponents in fruitless arguments while convincing onlookers over thousands of different social media accounts at once.

Prof Michael Wooldridge, director of foundation AI research at the UKs Alan Turing Institute, said AI-powered disinformation was his main concern about the technology.

Right now in terms of my worries for AI, it is number one on the list. We have elections coming up in the UK and the US and we know social media is an incredibly powerful conduit for misinformation. But we now know that generative AI can produce disinformation on an industrial scale, he said.

Wooldridge said chatbots such as ChatGPT could produce tailored disinformation targeted at, for instance, a Conservative voter in the home counties, a Labour voter in a metropolitan area, or a Republican supporter in the midwest.

Its an afternoons work for somebody with a bit of programming experience to create fake identities and just start generating these fake news stories, he said.

After fake pictures of Donald Trump being arrested in New York went viral in March, shortly before eye-catching AI generated images of Pope Francis in a Balenciaga puffer jacket spread even further, others expressed concern about generated imagery being used to confused and misinform. But, Altman told the US Senators, those concerns could be overblown.

Photoshop came on to the scene a long time ago and for a while people were really quite fooled by Photoshopped images then pretty quickly developed an understanding that images might be Photoshopped.

But as AI capabilities become more and more advanced, there are concerns it is becoming increasingly difficult to believe anything we encounter online, whether it is misinformation, when a falsehood is spread mistakenly, or disinformation, where a fake narrative is generated and distributed on purpose.

Voice cloning, for instance, came to prominence in January after the emergence of a doctored video of the US president, Joe Biden, in which footage of him talking about sending tanks to Ukraine was transformed via voice simulation technology into an attack on transgender people and was shared on social media.

A tool developed by the US firm ElevenLabs was used to create the fake version. The viral nature of the clip helped spur other spoofs, including one of Bill Gates purportedly saying the Covid-19 vaccine causes Aids. ElevenLabs, which admitted in January it was seeing an increasing number of voice cloning misuse cases, has since toughened its safeguards against vexatious use of its technology.

Recorded Future, a US cybersecurity firm, said rogue actors could be found selling voice cloning services online, including the ability to clone voices of corporate executives and public figures.

Alexander Leslie, a Recorded Future analyst, said the technology would only improve and become more widely available in the run-up to the US presidential election, giving the tech industry and governments a window to act now.

Without widespread education and awareness this could become a real threat vector as we head into the presidential election, said Leslie.

A study by NewsGuard, a US organisation that monitors misinformation and disinformation, tested the model behind the latest version of ChatGPT by prompting it to generate 100 examples of false news narratives, out of approximately 1,300 commonly used fake news fingerprints.

NewsGuard found that it could generate all 100 examples as asked, including Russia and its allies were not responsible for the crash of Malaysia Airlines flight MH17 in Ukraine. A test of Googles Bard chatbot found that it could produce 76 such narratives.

NewsGuard also announced on Friday that the number of AI-generated news and information websites it was aware of had more than doubled in two weeks to 125.

Steven Brill, NewsGuards co-CEO, said he was concerned that rogue actors could harness chatbot technology to mass-produce variations of fake stories. The danger is someone using it deliberately to pump out these false narratives, he said.

{{topLeft}}

{{bottomLeft}}

{{topRight}}

{{bottomRight}}

{{.}}

See original here:

Elections in UK and US at risk from AI-driven disinformation, say experts - The Guardian

Ethan Mollick, a professor at University of Pennsylvania's Wharton School of Business, compares AI to an intern who "lies a little bit," according to CBS News.Getty Images

UPenn professor Ethan Mollick compares AI to an "intern" who "lies a little bit," CBS reports.

Like interns, AI tools require guidance for their outputs to be useful, according to Mollick.

His thoughts on AI come as users adopt tools like ChatGPT to make their work and lives easier.

AI can be more than just your assistant it can also be an employer's intern, says one professor.

Ethan Mollick, a professor at University of Pennsylvania's Wharton School of Business, said that AI tools can be "good for a lot of things" despite its tendency to make factual errors. But that's not so different from humans, especially those who are new to the job market, he said.

"It's almost best to think about it as a person like an intern you have working for you," Mollick told CBS News in an interview this week when asked about AI's usefulness and limitations.

Similar to interns who may overcompensate to get ahead of the curve, Mollick compares AI to an "infinite intern" who "lies a little bit" and, at times, wants to make their bosses "a little happy."

Writing emails, Mollick says, is one way AI can be used to "help you overcome blockages in your every day life" and become "a better and more productive writer."

But like interns, AI requires guidance for its outputs to be useful.

"It's actually very useful across a wide variety of tasks, but not on its own," Mollick says. "You need to help it out."

When Insider reached out for comment, Mollick referred to his previous blog post that echoes the sentiment.

"I would never expect to send out an intern's work without checking it over, or at least without having worked with the other person enough to understand that their work did not need checking," Mollick wrote in his blog post. "In the same way, an AI may not be error free, but can save you lots of work by providing a first pass at an annoying task."

Story continues

Mollick's thoughts on AI come as generative AI tools like OpenAI's ChatGPT take the world by storm. As of January, more than 100 million users have flocked to the chatbot, some using it as a personal assistant to make their work and liveseasier.

In fact, Mollick, who teaches a class on entrepreneurship and innovation, requires his students to use ChatGPT to help with their classwork. Still, he recognizes that the chatbot isn't perfect.

"AI will never be as good as the best experts in a field,"Mollick told NPR in an interview. "We still need to teach people to be experts."

Read the original article on Business Insider

Continued here:

A Wharton professor says AI is like an 'intern' who 'lies a little bit' to make their bosses happy - Yahoo Finance

HBR EDITOR AMY BERNSTEIN: Nitin, youre a management consultant, you lead Deloittes global AI business. Whats the most interesting conversation youve had recently with a client?

DELOITTE PRINCIPAL NITIN MITTAL: A client, the CFO of the client basically said, If I apply generative AI in my company and the use case that, Nitin, you articulated took me, which is apply in a call center for customer care. Why? Because the marginal cost of conversing with that customer using a virtual digital agent is a zero, and because the marginal cost is zero, I know if I apply it itll drop my cost structure by 60 to 70%. But, what does it do to all the employees that I have who are from a disadvantaged part of the society Now the CFO was white, disadvantaged part of the society, who essentially are earning their daily living and have no other jobs?

AMY BERNSTEIN: I mean, that seems like a perfectly reasonable question. Howd you answer?

NITIN MITTAL: I punted it to a certain degree because its a difficult one to answer.

AMY BERNSTEIN: Yeah.

NITIN MITTAL: The reality is, yeah, itll lead to job losses. And the only way that youll be able to kind of overcome it, you have to reskill yourself for a different job as opposed to being in a call center. Reskill yourself, get a vocation training to be, for example, a prompt engineer who actually prompts and trains the models than being in the call center. The pay is probably the same, but there has to be a willingness both by the individual to get retrained and by the employer to do the retraining.

AMY BERNSTEIN: Welcome to How Generative AI Changes Everything, a special series from the HBR IdeaCast. Read just about any business history or any case study, and you realize just how much success depends on company culture. The unwritten rules of behavior can make the difference between capitalizing on a big shift or missing it all together. You cant have successful innovation without the right culture, you cant compete successfully without the right culture, you cant thrive over the long term without the right culture. And it follows that if you want to bring your organization into a future that includes generative AI, you need to build the right culture for it.

This week, How Generative AI Changes Organizational Culture. Im Amy Bernstein, editor of Harvard Business Review and your host for this episode. In this special series, were talking to experts to find out how this new technology changes workforce productivity and innovation, and were asking how leaders should adopt generative AI in their organizations and what it means for their strategy. Later on in this episode, youre going to hear from Harvard Business School professor Tsedal Neeley. Were going to talk through the known risks and how leaders can respond. But, first, Im talking to Nitin Mittal. He runs the global AI business at Deloitte, and he helped develop the firms own implementation of generative AI. Hes also a coauthor of the book, All-in On AI: How Smart Companies Win Big with Artificial Intelligence. Nitin, thanks for coming on the show.

NITIN MITTAL: Thank you.

AMY BERNSTEIN: When you walk into a clients organization, what are the signs that you look for that say, this organization is ready to move into AI?

NITIN MITTAL: First impressions dont always tell the whole story in terms of what an enterprise may be doing. But, having said that, if an organization already has some kind of a setup, like a center of excellence or a group that is focused on AI and has been experimenting and working with different business units, that is a very positive sign. On the other hand, if they just have a data science group that has been conducting proof of concepts without the connectivity to business, theyre not thinking about the culture of the organization, and they would very likely not be progressing. Those are things to kind of look out for. The other aspect to look out for is the leadership and the human side.

AMY BERNSTEIN: Yes. How do you advise your clients to lead, to shape their organizations into cultures that will embrace AI rather than run from it in fear?

NITIN MITTAL: Yeah. So, what is being noticed and what is being observed in many of these organizations is that the pressure to move ahead at speed and with skill is coming from the employees themselves. If we dont provide them these particular tools, and we dont provide them all the ways of augmenting themselves through generative AI, they are going to find their own ways, and that could lead to unfortunate circumstances where they end up using, lets say, open source models and start leaking an organizations data through the usage of those open source models.

AMY BERNSTEIN: So, you just alluded to the need for guardrails, right?

NITIN MITTAL: That is correct.

AMY BERNSTEIN: So, I wonder then what the role of culture is in all of this. I mean, is there a way communicate whats okay and whats not okay when you, an employee, are out there experimenting with ChatGPT, generative AI, which we want you to do within certain bounds, right? How does culture come into play here?

NITIN MITTAL: My view is that no AI system is going to magically somehow be responsible by itself without the culture of that organization being responsible onto itself to start with. It cannot be dictated by the CEO, it cannot be governed by the board, it cannot be mandated by the leadership. It is the prerogative, it is the sense of accountability of every single person to essentially always think about the right usages, in the right time, for the right areas where AI can be applied.

AMY BERNSTEIN: So, Nitin, as you talk to your clients, are you seeing alignment between the management team and boards, or misalignment? Whats going on there, on the generative AI front?

NITIN MITTAL: I would not necessarily say theres misalignment. Rather what it is say it is, its a lot about questions. The board certainly has a lot of questions of management, but management also has questions. And its all essentially around what is the impact to our business? How fast would that impact materialize? How disruptive this could be? And ultimately, how do we need to respond both culturally and from a safety and responsibility standpoint to this phenomena? Thats the set of questions being asked.

AMY BERNSTEIN: How are those questions being answered?

NITIN MITTAL: Frankly, theyre not necessarily being precisely answered. Everyone is trying to get their arms around it. We have a pretty good idea, but we also have to kind of learn. In Deloitte, for example, we have something called the trustworthy AI framework, and by its very nature, its a framework. It gives a set of guidelines, protocols, and methods in terms of what to think, when to apply those methods, and how to apply those methods. But, every organization also has to make sure that their employees are culturally sensitive to applying it in a responsible manner.

AMY BERNSTEIN: What does that mean?

NITIN MITTAL: The same way, the same way that every employee has a bond in terms of how do they work with their coworkers, how do they show up, what task they actually perform, and consequently, what is the team environment that they want? Think of that bond extending beyond just the human coworker, extending to essentially a non-carbon, non-bipedal coworker that happens to be an intelligent machine.

AMY BERNSTEIN: So, how do companies, that do it right, tease out that bond, that you just described, and turn it into a culture that can guide an organization forward on the use of AI?

NITIN MITTAL: There are perhaps not many companies who have kind of perfected it. But there are certain elements in terms of what is kind of critical to tease this out. First and foremost, education on cultural fluency. What would it take our employees to essentially apply things in a responsible manner, in a safe manner, for the benefit of not only the business, but their customers and society at large?

AMY BERNSTEIN: Does any organization train on cultural fluency in a way that you would want to share with other organizations.

NITIN MITTAL: In pockets. Ive seen it in pockets. Ive seen it in pockets in a few organizations that we serve. Ive also seen it in pockets in Deloitte as an example. But, that cultural fluency has typically extended to the realm of being culturally sensitive, particularly if youre a multinational organization, not necessarily culturally sensitive in the context of the rise of intelligent machines.

AMY BERNSTEIN: So, it sounds as if leaders then have to start making room for these foundational questions. I mean, these are questions weve never had to ask ourselves before, right?

NITIN MITTAL: These are questions we have never had to ask ourselves before, because now, with generative AI, that concept of we, the people, also transcends to we, the people and machines. And thats where the cultural boundaries have to be pushed. What would it mean for a factory worker to have a robot as a coworker? What would it mean for a professional consultant to have an AI model that is augmenting your particular kind of job, and augmenting and aiding the insights that you bring, and consequently being a coworker in your team? What does it mean for a medical professional to essentially have a AI assistant that is aiding with diagnosis? What does it mean?

AMY BERNSTEIN: So, this will call on everyones powers of imagination, but also everyones commitment to accountability and trust.

NITIN MITTAL: Absolutely. This is where I was kind of going earlier. It has to be for everyone, by everyone.

AMY BERNSTEIN: Nitin, youve described what progressive organizations are starting to do, including Deloitte. Where have you seen organizations kind of miss the mark? Where do they go wrong?

NITIN MITTAL: Well, there are definitely telltale signs of it.

AMY BERNSTEIN: Yeah, what are they?

NITIN MITTAL: Frankly, the organizations that absolutely miss the mark is who have got this viewpoint that, Well, this is yet another technology, probably going through a hype cycle, and consequently well just have kind of this particular group in IT, or this data science function that we have, or this set of individuals, kind of just look into it and take it forward. That is when they miss the mark. Rather, those organizations who actually view this as a moment in time where they have to question the basis of how do they compete, how do they thrive, what changes do they need to make, both from a product or a service perspective, but more important from a culture and a people standpoint, actually are the ones who are able to progress forward. If that can be tackled first, you will be a learning organization, you will thrive in a digital economy, and you will redefine the market that youre in.

AMY BERNSTEIN: Nitin, thank you so much.

NITIN MITTAL: Well, thank you.

AMY BERNSTEIN: Coming up after the break, I talk to Harvard Business School professor Tsedal Neeley about adopting generative AI in your organization, and the right ways to do that effectively and ethically. Stay with us.

AMY BERNSTEIN: Welcome back to How Generative AI Changes Organizational Culture. Im Amy Bernstein. Joining me now to discuss how to adopt generative AI within your own company is Tsedal Neeley. Shes a professor at Harvard Business School, and she wrote the HBR Big Idea article, 8 Questions About Using AI Responsibly, Answered. Tsedal, thanks for joining me.

HBS PROFESSOR TSEDAL NEELEY: Im so happy to be with you, Amy. Thank you for having me.

AMY BERNSTEIN: Im so happy youre here. So, I have more than eight questions to ask you, all right?

TSEDAL NEELEY: Great!

AMY BERNSTEIN: In your research, youve studied how global companies and smaller organizations alike become leaders at digital collaboration, remote work, and hybrid work. What about generative AI? Are organizations set up for it?

TSEDAL NEELEY: Currently, organizations are neither set up for it, nor do they fully understand it, but the adoption and the curiosity around it has been extraordinary, and so I think people will start figuring it out very quickly.

AMY BERNSTEIN: What kinds of changes are needed? Theyre cultural, theyre organizational, what kind?

TSEDAL NEELEY: I think the first thing that organizations need to ensure happens is that people understand these technologies fully. To really develop some form of fluency, a minimum level of fluency around what the technology is, what it isnt, what are the limitations, what are the risks, and what are the opportunities. So, everyone needs to start experimenting with it, but its really important to do it very carefully.

AMY BERNSTEIN: Now, I have to raise the specter of change management. What does this mean for change management? Its hard enough under the up till now normal circumstances.

TSEDAL NEELEY: Absolutely. You know what? Imagine change getting motivated from the top-down imperatives or mandates. Here we have a scenario where theres a lot of bottom-up activities.

AMY BERNSTEIN: With what youre describing, so much bottom-up rather than top-down change. What is leadership then?

TSEDAL NEELEY: Leadership, in this kind of scenario, you need digital leaders with digital mindsets to very quickly mobilize and begin organization led experiments and implementations of these tools, because otherwise, youre going to have individuals just experimenting and playing with them, which is actually a very, very good thing, but not understanding how they work. You can easily and unwittingly make a very consequential mistake to an organization. An example of this is uploading proprietary information, organizational confidential materials, because anything you put into these systems get fed into the overall model, which is why leaders have to guide the way these things are implemented. We need to think about these tools no different than the way that all of us had access to the internet 30 years ago. You cant stop it, you cant control it, unless you set the right boundaries and have these ethical codes that people follow, and even ways to protect the company.

AMY BERNSTEIN: So, do we need a new playbook to manage this change?

TSEDAL NEELEY: We need to take our playbook and add technology and speed and buy in and learning onto it. Organization-led opportunities and experiments become important, which is have some people start to work with them and document what theyre learning. Also, think about where do we automate and where are the places where we can do different types of strategic, creative, interpersonal kind of work. Third thing is you have to have a culture of responsible AI use from the start. This is not an afterthought, this has to be embedded in all that you do from the start. People need to be trained, and every single decision they make with their generative AI uses has to have ethical considerations, because its easy to get in trouble around this. Then finally, I would say that you have to pilot, you have to iterate, you have to be open to continuous learning, constant adapting, because you have to have a communication plan where people are open and understand that these changes are happening so fast that we have to be attuned to them and be prepared to implement them. Then finally, the culture change. You have to encourage a culture of flexibility, of innovation, of continuous learning, rewarding people who are adopting the new technologies in the right ways, you have to provide support and resources for those who are struggling, and for those many people are very afraid of these changes. So, youve got to make sure no one gets left behind. This type of change requires skill building, shifts to the nature of work in your organization. Many, many shifts.

AMY BERNSTEIN: So, Tsedal, talk a little bit about skill building, because that can be pretty challenging. You have people who are starting out with different skill levels, but also very different attitudes and levels of acceptance and fear. How do you do skill building in an organization with this nascent technology coming on so hard?

TSEDAL NEELEY: So, imagine a two by two. You know? Youre with an HBS professor, we have to come out with a two by two. You should have expected this.

AMY BERNSTEIN: Knew it, knew it.

TSEDAL NEELEY: And you knew it. Imagine a two by two, and imagine a framework called the Hearts and Minds framework. On one side, you have buy-in, where people have to believe that this is important. And another dimension is the belief that they could do it. Or another word for this is, do I have the self-efficacy for this? So, if you have high buy-in and high sense of efficacy, you are going to be inspired, excited. You are in a great, great spot. But, for those who do not, who may be struggling, it is incumbent on leaders of organizations to do the right type of messaging, to also build awareness and provide resources and support for people to learn these things.

AMY BERNSTEIN: How do you do this at scale over time? How do you sustain this?

TSEDAL NEELEY: Its actually something that weve done many times when it comes to scale building. Number one, individual managers need to understand where their team members are. So, youre bringing it down to the unit of analysis of a team. Team managers need to understand where people are in terms of their buy-in and their sense of efficacy. With that, has to be an organized training guide, learning guide, tutorials. Continuous learning is a mandate in this era of dramatic technological shifts and changes.

AMY BERNSTEIN: It sounds like its sort of the actual learning along with the compassionate piece of leadership, helping people embrace it, the hearts and minds piece.

TSEDAL NEELEY: Absolutely. Do you need to help more with the mind part or do you need to help more with the heart part? The other thing Ill say here is theres a phenomenon in this type of change called contagion. We do this as a group, together we have collective efficacy, and together we get through it. You cant let individuals flounder and get into sense of job insecurity, et cetera. This is why the team level is so important for this.

AMY BERNSTEIN: Thats a great insight. It puts so much of the agency into the hands of the manager, the team leader, it doesnt just happen from the top-down.

TSEDAL NEELEY: Absolutely.

AMY BERNSTEIN: Yes.

TSEDAL NEELEY: It needs to touch every member of the organization.

AMY BERNSTEIN: So, when were talking about hearts and minds, Tsedal, we really have to talk about the fear factor as well. A lot of tasks are going to get automated, and the natural conclusion for many of us to draw is that we will get automated out of our jobs. What do you say to that? What do you say, Tsedal, and what should a manager say to his or her team?

TSEDAL NEELEY: Listen, theres no doubt that theres going to be changes to the nature of jobs. Peoples jobs will shift. But, one thing we know is that every technological revolution has created more jobs than it has destroyed. For many people who are writers, writers are panicked, and I understand that completely, but its important to understand that these technologies work well with humans in the loop, meaning its human intelligence meeting artificial intelligence. Now, the reality is the long-term effects of generative AI are not fully known to us. We know theyre going to be complex, we know theres going to be periods of job displacement, theres going to be a period of job destruction for some industries, and this is why I always come back to the notion of education, training, upskilling and reskilling, and thinking about the various ways in which generative AI cannot help us with interpersonal work, with empathy, with various forms of creativity. So, theres a lot for us to continue to do, but its important to understand that, ultimately, we cant even conceive of the new things that are going to come out of this. So, there will be many more opportunities, many more things, many more industries that we cant even imagine that are going to be formed. Will things remain the same for individuals in terms of jobs, companies and industries? Unlikely.

AMY BERNSTEIN: So, its a very new technology, we dont have a lot of guardrails around it, we dont even really know what its capable of, we get a taste of it if we play with it. What are some of the risks ethically speaking here?

TSEDAL NEELEY: So, generative AI comes with many risks. The first one is it can perpetuate harmful biases, meaning deploying negative stereotypes or minimizing minority viewpoints, and the reason for this is the source of the data for these models, the underlying models, the large language models, are the internet, documents, its really pulling from everywhere and anywhere. As long as we have biases and stereotypes in our societies, these language models will have them as well. The second thing is misinformation from making stuff up, falsehoods, or violating the privacy of individuals by using data that is ingested, embedded in these models without the consent of people, so personal data can get into these. So, these are the ethical considerations that are important to both understand and to develop codes of ethics in your organizations to avoid them, and there are ways to avoid them. By the way, regulation is coming fast, the government is working on it at the state level, at the national level, but regulation still lag adoption.

AMY BERNSTEIN: Lets talk about harmful bias a bit. How do we prevent it?

TSEDAL NEELEY: There are a couple of things to consider. One is to always understand the source of data. So, generative AI may not give you citations or even the right citations, but if theres some information that it spits out, its important to check it and to double check it and to triple check it, to triangulate, to try to find primary sources. So, its important to have diversity in your company to vet these things, and if youre building models, large language models, internal large language models, which is where I think this is going to go for many companies, you need diversity, you need women, you need people of color, who are helping design these systems, you need to set strict rules around documentation, transparency, understanding where the source of all of this data is coming from.

AMY BERNSTEIN: Doing the legwork.

TSEDAL NEELEY: Absolutely, must do the legwork.

AMY BERNSTEIN: Yeah. Yeah. Theres a job that isnt going away, huh?

TSEDAL NEELEY: Exactly. These tools, in my mind, get us started, and we need to do additional work before the output is ready for primetime.

AMY BERNSTEIN: So, you mentioned transparency. What about how you, your team, your organization, is using generative AI? What are the responsibilities there in terms of transparency?

TSEDAL NEELEY: Its interesting because I dont think everyone will be reporting that theyve used ChatGPT for any and every little thing. I mean, thats no different than do we go around telling people, I Googled this, I Googled that. I went on this website, I went on that website, the use of our browser? No way are we going to do that, and no way do we need to do that. It only matters if there are important consequences from the use of these tools.

AMY BERNSTEIN: Right, and I guess it goes back to what you were saying before about citations and double checking that you, as the individual using these tools, have to remember that youre responsible for the truth-

TSEDAL NEELEY: Absolutely.

AMY BERNSTEIN: that youre putting out there. You cannot blame GenAI for your mistakes, because theyre your mistakes.

TSEDAL NEELEY: Theyre your mistakes, and this is where cultural change is important. The responsible AI use culture is going to be crucially important. This is why this is such a big deal for companies. Each individual user has to be responsible for what they put out in the world in their organization by using these tools, which means they have to be extra thoughtful, they have to be extra careful, they have to verify. The oversight is incredibly important. But, is it a shortcut tool? Is it a cheating tool? Absolutely not. We need to celebrate these tools because theyre not going away, and we need to guide people on their best uses.

AMY BERNSTEIN: Right. So, the skills you need are both technical, and then its those timeless leadership skills around integrity and accountability and a sense of fairness, right?

TSEDAL NEELEY: A hundred percent. In fact, the timeless leadership skills will be more important than ever before, because right now were a bit in the wild, wild west, and we inside of our organizations need to determine what are the safeguards? What are the guardrails? What are the ways in which were going to advocate people use these? So that we get the best possible results from them, without getting ourselves, as an organization, in trouble, or without any individuals unwittingly getting themselves in trouble.

AMY BERNSTEIN: So, then, given the kind of small d democratic nature of these tools, how do organizational leaders instill those values to ensure that these tools are used in a way that is fair and equitable?

TSEDAL NEELEY: I love that question, because it takes me right back to one of the most powerful organizational characteristics called trust. Trust, a culture where there is trust, a culture where you have some rules to help people, but you trust people to make the right decisions because leaders are role modeling it. Theres learning and training to help people understand how to use it, and the belief that one of our shared values in our organization is trust. This is no different than hybrid work where you trust people after youve equipped them. Its that same characteristics. I am learning that the more digital we become, the more trust becomes one of the most important shared values that companies need to uphold.

AMY BERNSTEIN: You know, what I find so inspiring about your message, Tsedal, is that youre saying you have got to do the work, youve got to understand this technology as a technology, but equally, you have got to pay attention and communicate as a leader, all those timeless leadership skills, the ones we just discussed, because in order to foster the kind of trust you are describing, you have to communicate not just your competence with the tool, but the values that you bring to its use, and thats the contagion, right?

TSEDAL NEELEY: Thats exactly right. That you cant be a mediocre leader in the world of remote or hybrid work. You cannot be a mediocre leader in the world of generative AI that is poised to transform every organization, every industry in ways that we cant really understand today. So, your leadership fundamentals are incredibly important, and leaders have to lead, they cant micromanage, you cant micromanage your way out of generative AI. Thats impossible. People are using it whether you want it or not. The question is, how do you make sure that you lead the way on generative AI in your organization as opposed to reactively run around trying to damage control? Because it can bring damage too.

AMY BERNSTEIN: So, whats changed is the technology, but the leadership values remain as theyve always been.

TSEDAL NEELEY: The leadership values remain with less flexibility on poor leadership. Theres no hiding on this one, youve got to be right ahead, and every leader has to work on becoming a digital leader with a digital mindset. This is it.

AMY BERNSTEIN: Tsedal, it was so interesting to talk to you. Thank you.

TSEDAL NEELEY: Thank you so much, Amy.

AMY BERNSTEIN: Anytime. Thats Tsedal Neeley, a professor at Harvard Business School. She wrote the article 8 Questions About Using AI Responsibly, Answered. You can find it, and other articles, by experts at hbr.org/techethics. Before that, I talked to Nitin Mittal. He leads Deloittes global AI business and co-wrote the book All-in On AI: How Smart Companies Win Big with Artificial Intelligence.

AMY BERNSTEIN: Next episode, How Generative AI Changes Strategy. HBR editor in chief Adi Ignatius will talk to experts who take stock of the competitive landscape and share how to navigate it effectively for your organization. Thats next Thursday, right here in the HBR IdeaCast feed after the regular Tuesday episode. This episode was produced by Curt Nickisch. We get technical help from Rob Eckhardt. Our audio product manager is Ian Fox, and Hannah Bates is our audio production assistant. Special thanks to Maureen Hoch. Thanks for listening to How Generative AI Changes Everything, a special series of the HBR IdeaCast. Im Amy Bernstein.

Read more:

How Generative AI Changes Organizational Culture - HBR.org Daily

Artificial intelligence is expected to revolutionize businesses across the globe, and those in the sharing economy are no exception. There is nearly $6 trillion in revenue opportunity from AI across the internet industry, a March report from Morgan Stanley found. The latest AI craze, generative AI , has companies across the country looking to capitalize on the trend. "Every single company faces the challenge today of deciding how to distribute its IT budgets such that it can get enough artificial intelligence to deliver improvement in costs, improvement in revenue operational value and open an avenue to transformation," Gartner analyst Whit Andrews said. "Every single company there is nobody who gets a pass at this point." For companies in the sharing economy, such as Uber , Lyft and DoorDash , AI is already a way of life. People call up a ride or a food order on an app and they are matched with drivers to either take them to their destination or deliver their food. Yet the effect of the technology is just beginning. "UBER/LYFT/DASH already use ML [machine learning] in their matching algorithms (matching rides/eaters with drivers/couriers)," Morgan Stanley wrote in its report. "That said, we see further improvements in fleet utilization and matching lower wait times and pricing and higher profitability." AI tailwind for Uber Uber has both its ride-sharing service and UberEats food delivery business. When the company reported earnings earlier this month, CEO Dara Khosrowshahi said Uber has a "significant data advantage" that allows it to employ AI solutions and is already using AI to predict "highly accurate" arrival times for rides and deliveries. Even still, it's early innings. "We are just starting to understand the capabilities of AI and we are a long way from understanding its potential," Khosrowshahi told CNBC after the earnings report. UBER mountain 2019-05-10 Uber's performance since is May 10, 2019 IPO The earliest and most significant effect of AI will be on its developer productivity, the company said on its earnings call. You'll also have more chatbots powering experiences, which saves on costs. "Then we will look to surprise and delight. 'Pick me up at the airport. I'm arriving on American flight 260 on Tuesday,'" Khosrowshahi said. "We will know who you are, where your home is, what kind of cars you like, et cetera." According to Morgan Stanley, AI and machine learning will be a tailwind to network efficiency. On the rider side, every 1% increase in rider frequency and 20 basis points increase in the rides take rate would lead to 1% incremental company revenue and 3% of incremental company earnings before interest, taxes, depreciation and amortization. On the delivery side, every 1% increase in rider frequency and five basis point increase in the rides take rate would lead to 0.4% incremental company revenue and 1% of incremental company EBITDA. For investor Sarat Sethi, who owns shares of Uber, the company's use of the technology helped them become efficient and puts them ahead of the competition. "They were really on the forefront," said Sethi, portfolio manager at Douglas C. Lane & Associates. "Now we've seen the results over the last few quarters, where the efficiencies are really coming through. And Uber is just understanding more and more of the customer and the more and more data they get." Tech investor Gene Munster, partner at Deepwater Asset Management, is also bullish on Uber's ride-sharing and UberEats business because he believes the company has persistent growth. One of the reasons he's really excited about its AI prospects is the autonomous deliveries and transportation opportunities. He sees a move toward autonomy, although not the entire fleet, which brings the potential for higher margins. He also thinks customers will be willing to book autonomous cars if it saves them money. "Autonomy will drive down the cost per mile for the customer, which will increase use, but it should increase margin at the same time, which is pretty unique," said Munster, whose firm owns shares of Uber. AI's effect on the sharing economy There are several ways AI can boost ridership or food delivery orders for those in the sharing economy. More accurate natural language processing could help with search and help create better recommendations for users, Morgan Stanley said. AI will also be able to better anticipate consumer behavior. "For the rideshare businesses, this could take the form of better allocation of supply to meet rider demand as algorithms are better able to predict where influx of demand will next occur and point drivers in that direction," the firm said. For the delivery businesses, it could mean better suggestions or automatic orders for groceries. AI may also help in the generation of new business opportunities, particularly in delivery, Morgan Stanley's analysts said. "Greater ability to predict customer behavior potentially minimizes the initial capital investment risk for companies looking to build their own supply and adds certainty that there will be demand for the product once created," they said. As AI gets smarter, it can also help boost productivity and automate tasks now performed by humans. What every company in the sharing economy is trying to sort out now is why people are involved with certain tasks, Gartner's Andrews said. "You have to be able to answer the question. You have to be able to say there are people involved with this because it demands the creativity and the originality of human perspective," he said. "If it lacks that, it's going to get automated. We are in the process of taking this enormous step toward that new reality." Companies also have to continue investing in the newest technology or risk being left behind and it's not cheap. "The companies have tremendous opportunity to evolve their model. Now it is just about execution," said Baird technology strategist Ted Mortonson. DoorDash's AI experiments Similar to its sharing economy peer Uber, DoorDash is also hoping to optimize its operations and improve productivity with AI in the near term, said Rohit Kulkarni, senior research analyst at Roth MKM. Looking ahead, it's about creating a better consumer experience by using generative AI, he said. "What DoorDash can do is better content discovery, which goes back to consumer experiences and how AI can put the right content in front of the right consumer at the right time," said Kulkarni, who has a neutral rating on the stock. His $72 price target implies 10% upside from Wednesday's close. In fact, right now, DoorDash is running different experiments internally with generative AI, said Alok Gupta, the company's head of artificial intelligence and machine learning. "One of the things we're trying to understand with this new wave of generative AI tools is which ones are going to best serve the needs of certain features," he said. The company is looking at the quality of the tools, including if it gives the right answers, the price, the scalability and how it affects data privacy, security and ethics. "We're looking at the different generative AI vendors, we're looking at open-source models that we can host internally, and then we'll pick and choose," Gupta said. DASH mountain 2020-12-09 DoorDash's performance since its Dec. 9, 2020 IPO DoorDash already uses AI and machine learning to personalize the experience for consumers, help merchants achieve their sales goals by seeing which items are trending in their neighborhoods and to refine the timing of pickups for the drivers, or dashers. Generative AI will be able to further personalize and tailor the experience for users. Customers would see menus that better match their preferences and the interaction would be more conversational, Gupta said. It will also help with internal productivity, such as digitizing menu items, where to park and store locations. While Gupta can't quantify the financial effect for the company, he said AI will drive growth. "We strongly believe that if we improve the quality predictability of the experience of each of our audiences, that will naturally translate into better retention for audiences and how they use us, and that will help us," he said. However, Morgan Stanley estimates every 1% improvement in order frequency and five basis points improvement in take rate results in a $149 million, or 1.4%, uplift to company revenue and an $82 million, or 5%, improvement in EBITDA. "The extent to which AI drives substantial improvements in top-line growth could lead to teens upside [for the stock]," Morgan Stanley said. Lyft's difficulties Lyft has been struggling and losing market share to Uber. The ride-sharing company debuted on the public market in 2019 at $72 a share and is now trading below $10. Over the past year, its stock has dropped nearly 58%, plagued by disappointing earnings . Earlier this month, Lyft reported an adjusted loss of 7 cents per share for the first quarter, a penny more than expected, according to Refinitiv. The company also provided guidance for second-quarter sales and EBITDA that was less than expected. However, Lyft's new CEO, David Risher, is trying to make changes to right the ship, including layoffs and the launch of a new airport preorder feature. 'Biggest death star' As companies within the ride-sharing economy look to invest in the latest AI and use it to become more profitable, there may be some big competitors looking to swoop in, Baird's Mortonson said. The "cloud titans" that have massive balance sheets and free cash flow, as well as the "massive compute scale," could decide to move into the ride-sharing or food delivery business, he said. "Their biggest death star is Amazon ," Mortonson said. Not only does the e-commerce giant have the intellectual property that centers around AWS, but it also knows all about the next-generation logistics, routing and delivery, he said. "Their extension on delivery into food or other services they just have to turn the switch," he said. CNBC's Michael Bloom contributed reporting.

See the original post:

A.I. and sharing economy: UBER, DASH can boost profits investing ... - CNBC