Archive for the ‘Artificial Intelligence’ Category

MVP versus EVP: Is it time to introduce ethics into the agile startup model? – TechCrunch

The rocket ship trajectory of a startup is well known: Get an idea, build a team and slap together a minimum viable product (MVP) that you can get in front of users.

However, todays startups need to reconsider the MVP model as artificial intelligence (AI) and machine learning (ML) become ubiquitous in tech products and the market grows increasingly conscious of the ethical implications of AI augmenting or replacing humans in the decision-making process.

An MVP allows you to collect critical feedback from your target market that then informs the minimum development required to launch a product creating a powerful feedback loop that drives todays customer-led business. This lean, agile model has been extremely successful over the past two decades launching thousands of successful startups, some of which have grown into billion-dollar companies.

However, building high-performing products and solutions that work for the majority isnt enough anymore. From facial recognition technology that has a bias against people of color to credit-lending algorithms that discriminate against women, the past several years have seen multiple AI- or ML-powered products killed off because of ethical dilemmas that crop up downstream after millions of dollars have been funneled into their development and marketing. In a world where you have one chance to bring an idea to market, this risk can be fatal, even for well-established companies.

Startups do not have to scrap the lean business model in favor of a more risk-averse alternative. There is a middle ground that can introduce ethics into the startup mentality without sacrificing the agility of the lean model, and it starts with the initial goal of a startup getting an early-stage proof of concept in front of potential customers.

However, instead of developing an MVP, companies should develop and roll out an ethically viable product (EVP) based on responsible artificial intelligence (RAI), an approach that considers the ethical, moral, legal, cultural, sustainable and social-economic considerations during the development, deployment and use of AI/ML systems.

And while this is a good practice for startups, its also a good standard practice for big technology companies building AI/ML products.

Here are three steps that startups especially the ones that incorporate significant AI/ML techniques in their products can use to develop an EVP.

Startups have chief strategy officers, chief investment officers even chief fun officers. A chief ethics officer is just as important, if not more so. This person can work across different stakeholders to make sure the startup is developing a product that fits within the moral standards set by the company, the market and the public.

They should act as a liaison between the founders, the C-suite, investors and the board of directors with the development team making sure everyone is asking the right ethical questions in a thoughtful, risk-averse manner.

Machines are trained based on historical data. If systemic bias exists in a current business process (such as unequal racial or gender lending practices), AI will pick up on that and think thats how it should continue to behave. If your product is later found to not meet the ethical standards of the market, you cant simply delete the data and find new data.

These algorithms have already been trained. You cant erase that influence any more than a 40-year-old man can undo the influence his parents or older siblings had on his upbringing. For better or for worse, you are stuck with the results. Chief ethics officers need to sniff out that inherent bias throughout the organization before it gets ingrained in AI-powered products.

Responsible AI is not just a point in time. It is an end-to-end governance framework focused on the risks and controls of an organizations AI journey. This means that ethics should be integrated throughout the development process starting with strategy and planning through development, deployment and operations.

During scoping, the development team should work with the chief ethics officer to be aware of general ethical AI principles that represent behavioral principles that are valid in many cultural and geographic applications. These principles prescribe, suggest or inspire how AI solutions should behave when faced with moral decisions or dilemmas in a specific field of usage.

Above all, a risk and harm assessment should be conducted, identifying any risk to anyones physical, emotional or financial well-being. The assessment should look at sustainability as well and evaluate what harm the AI solution might do to the environment.

During the development phase, the team should be constantly asking how their use of AI is in alignment with the companys values, whether models are treating different people fairly and whether they are respecting peoples right to privacy. They should also consider if their AI technology is safe, secure and robust and how effective the operating model is at ensuring accountability and quality.

A critical component of any machine learning model is the data that is used to train the model. Startups should be concerned not only about the MVP and how the model is proved initially, but also the eventual context and geographic reach of the model. This will allow the team to select the right representative dataset to avoid any future data bias issues.

Given the implications on society, its just a matter of time before the European Union, the United States or some other legislative body passes consumer protection laws governing the use of AI/ML. Once a law is passed, those protections are likely to spread to other regions and markets around the world.

Its happened before: The passage of the General Data Protection Regulation (GDPR) in the EU led to a wave of other consumer protections around the world that require companies to prove consent for collecting personal information. Now, people across the political and business spectrum are calling for ethical guidelines around AI. Again, the EU is leading the way after releasing a 2021 proposal for an AI legal framework.

Startups deploying products or services powered by AI/ML should be prepared to demonstrate ongoing governance and regulatory compliance being careful to build these processes now before the regulations are imposed on them later. Performing a quick scan of the proposed legislation, guidance documents and other relevant guidelines before building the product is a necessary step of EVP.

In addition, revisiting the regulatory/policy landscape prior to launch is advisable. Having someone who is embedded within the active deliberations currently happening globally on your board of directors or advisory board would also help understand what is likely to happen. Regulations are coming, and its good to be prepared.

Theres no doubt that AI/ML will present an enormous benefit to humankind. The ability to automate manual tasks, streamline business processes and improve customer experiences are too great to dismiss. But startups need to be aware of the impacts AI/ML will have on their customers, the market and society at large.

Startups typically have one shot at success, and it would be a shame if an otherwise high-performing product is killed because some ethical concerns werent uncovered until after it hits the market. Startups need to integrate ethics into the development process from the very beginning, develop an EVP based on RAI and continue to ensure AI governance post-launch.

AI is the future of business, but we cant lose sight of the need for compassion and the human element in innovation.

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MVP versus EVP: Is it time to introduce ethics into the agile startup model? - TechCrunch

New Empirical Research Report on Artificial Intelligence Platforms by Forecast From 2022 to 2028 With Covid-19 Impact Analysis and Future Business…

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New Empirical Research Report on Artificial Intelligence Platforms by Forecast From 2022 to 2028 With Covid-19 Impact Analysis and Future Business...

IP&MC2022 Interdisciplinary Topics – Information Processing and Management Conference – Knovel

Note: This special issue is a Thematic Track at IP&MC2022. For more information about IP&MC2022, please visit https://www.elsevier.com/events/conferences/information-processing-and-management-conference.

IP&MC2022 INTERDISCIPLINARY-TOPICS (VSI: IPMC2022 INTERDISCIPLINE)

University: Huazhong University of Science and Technology

Email: yangyanwu@hust.edu.cn

University: Beijing Jiaotong University

Email: cjzhang@bjtu.edu.cn

University: Sun Yat-sen University

Email: mnswq@mail.sysu.edu.cn

University: Naval Academy Research Institute

Email: christophe.claramunt@gmail.com

This track at IP&MC2022 and a special issue in Information Processing and Management will publish cutting-edge original research at the intersection of information science, computing, artificial intelligence, economics, and social science concerning theory, methods, or applications in a range of domains, including but not limited to information science, information technology, management, advertising and marketing, energy, health, economics and social computing, and metaverse.

The track aims to serve the interests of primary researchers but also practitioners in furthering knowledge at the intersection of information science, computing, artificial intelligence, and social science by providing an effective forum for the timely dissemination of advanced and topical issues. The track is especially interested in original research articles, research survey articles, research method articles, and articles addressing critical applications of research.

Specifically, the track is interested in four types of manuscripts, which are:

We invite authors to submit their research work (including full-length, original, and unpublished research papers based on theoretical or experimental contributions and review studies), especially in areas of information science, information technology, management, advertising and marketing, energy, health, economics and social computing, and metaverse.

Topics of interest include, but are not limited to:

Submit your manuscript to the Special Issue category (VSI: IPMC2022 INTERDISCIPLINE) through the online submission system of Information Processing & Management. https://www.editorialmanager.com/ipm/

Authors will prepare the submission following the Guide for Authors on IP&M journal at (https://www.elsevier.com/journals/information-processing-and-management/0306-4573/guide-for-authors). All papers will be peer-reviewed following the IP&MC2022 reviewing procedures.

The authors of accepted papers will be obligated to participate in IP&MC2022 and present the paper to the community to receive feedback. The accepted papers will be invited for revision after receiving feedback on the IP&MC 2022 conference. The submissions will be given premium handling at IP&M following its peer-review procedure and, (if accepted), published in IP&M as full journal articles, with also an option for a short conference version at IP&MC2022.

Please see this infographic for the manuscript flow:https://www.elsevier.com/__data/assets/pdf_file/0003/1211934/IPMC2022Timeline10Oct2022.pdf

For more information about IP&MC2022, please visit https://www.elsevier.com/events/conferences/information-processing-and-management-conference.

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IP&MC2022 Interdisciplinary Topics - Information Processing and Management Conference - Knovel

Artificial Intelligence (AI) in Beauty and Cosmetics Market to Witness Huge Growth by 2029 | L’Oral’s (Modiface, Hair Coach), Beiersdorf (NIVEA SKiN…

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LOrals (Modiface, Hair Coach), Beiersdorf (NIVEA SKiN GUiDE), Olay (Skin Care App), Shiseido (Optune System), CRIXlabs (DBA Quantified Skin), Procter & Gamble (Opte Wand), Yours Skincare, My Beauty Matches, EpigenCare Inc., mySkin, Haut.AI, Luna Fofo, Revieve, ANOKAI. CA., Youth Laboratories, Pure & Mine, Glory Skincare, Nioxin, New Kinpo Group, Perfect Corp, Symrise (Philyra), Function of Beauty LLC, Coty Inc. (Rimmel), Este Lauder, Sephora USA, Inc. (Virtual Artist), Spruce Beauty , Givaudan, Beautystack and Polyfins Technology Inc.

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Table of Contents

Global Artificial Intelligence (AI) in Beauty and Cosmetics Market Research Report 2022 2029

Chapter 1 Artificial Intelligence (AI) in Beauty and Cosmetics Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Artificial Intelligence (AI) in Beauty and Cosmetics Market Forecast

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Artificial Intelligence (AI) in Beauty and Cosmetics Market to Witness Huge Growth by 2029 | L'Oral's (Modiface, Hair Coach), Beiersdorf (NIVEA SKiN...

Artificial Intelligence May Be Just Code, But Its Our Code – Forbes

AI

Theres nothing magical about artificial intelligence, its simply code designed by fallible humans using fallible data. The magic comes from the humans working with or seeing the benefits of AI. So the questions are: are we expecting too much from AI? Too what extent should companies and their executives rely on the output delivered by AI?

This was the subject of debate at a panel hosted at AI Summit in New York, held in early December, focusing on risks in the emerging role of AI in the financial services sector, but the discussion had wide-ranging implications across all industries. (I had the opportunity to co-chair the conference, and moderate the panel.)

We think AI is telling us something, but its not, cautioned Rod Butters, chief technology officer for Aible. Its just a bunch of code. It doesnt know. This is the fantasy we all fall into. Somehow we think that model has embodies something. The reality is that an AI is just a statistical engine, and in a lot of cases, its a bad statistical engine.

With AI these days, the biggest systemic risk in the notion that artificial intelligence is artificial, said Rik Willard, founder and managing director of Agentic Group, and member of the advisory board of the World Ethical Data Foundation. Its all done by humans; its all manifested by humans. When we look at risk versus returns, its only as good as the financial institutions, and the regulatory frameworks around those institutions. Are we supporting the same human and economic algorithms that we set up before technology, or are we working to make those better and more inclusive?

In addition, AI is still a relatively immature technology, said Drew Scarano, vice president of global financial services at AntWorks. Ten years ago we werent even talking about AI, but today, its a multi-billion dollar industry, he said. said Scarano. We might be too reliant on this technology, forgetting about the humans in the loop and how they play an integral part in complementing artificial intelligence in order to get desired results.

Another challenge is AI systems tend to get built in relative isolation. AI is just code, and the people building these systems may have limited perspectives on its value to the business, Butters cautioned. When we tell data scientists go out and create a model, were asking them to be a mind reader and a fortune teller, he said. Those are two bad job sets, it doesnt work. The data scientist is trying to do the right thing, creating a responsible and solid model, but based on what? Ultimately when they build a model, unless theyve got this combination to create transparency, create expandability, actually communicate that across to the business constituency both at a strategic and tactical, who is in charge? Just creating a great model does not necessarily solve all problems.

In the process of building data models, data scientists need to understand the objectives of the enterprise, taking into account the human implications, Scarano said. You can have engineer build a great bridge. So if its not going over what its intended to do, its just a great bridge, right? Im afraid that people in business, especially financial services. will just keep relying too much on technology. We need a holistic approach, in coexistence with humans.

Look beyond the technology and statistics of AI, and focus on what ultimately serves the customer, Scarano urged. Its about how we complement humans with artificial intelligence to drive business, and also drive customer reality, customer success and customer satisfaction at the end of the day.

The path to AI in service of business objectives relies on the establishment of consistent frameworks that guide its development, panelists agreed. I was raised in a fail-fast environment, said Willard. You build code, you test, and fix what's broken. You fix it on the fly. You build it, it kind of works, you let it loose, then you refine it over time based on input to the feedback loop. However, with AI, the issue is that we put it in a position of judgment. Like in the criminal justice system, where it does a lot of harm before you get it right. In the banking system its loan, no loan; score, no score; or credit, no credit. How do we build testing frameworks and sandboxes that have the accuracy thats necessary to be launched at scale, while doing less harm along the way?

AI is being used for many purposes across the financial services industry, but the risk is in de-humanizing the interpersonal qualities that helped build the industry. Today we can use AI for anything from approving a credit card to approving a mortgage to approving any kind of lending vehicle, said Scarano. But without human intervention to be able to understand there's more to a human than a credit score, there's more to a person than getting approved or denied for a mortgage.

Customer experience is the foundation of financial services, and this needs to be front and center of all AI initiatives. There needs to feedback loops in AI-driven systems that incorporate human input. As we implement AI-based solutions, we need to ensure that the end users, the customers, who are consuming the product are also happy with that investment and solution as well, said Robert Magno, solutions architect with Run:AI. It makes a lot of sense to have robots moving packages around, automated in a warehouse. But from a customer service standpoint, if a person interacting with a chatbot is getting frustrated, there needs to be a feedback loop to ensure solutions you're implementing are resonating with your customers, and they're enjoying the experience as much as you're enjoying creating the experience.

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Artificial Intelligence May Be Just Code, But Its Our Code - Forbes