Monthly Archives: August 2022

It started with a single tweet in November 2019. David Heinemeier Hansson, a high-profile tech entrepreneur, lashed out at Apples newly launched credit card, calling it sexist for offering his wife a credit limit 20 times lower than his own.

The allegations spread like wildfire, with Hansson stressing that artificial intelligence now widely used to make lending decisions was to blame. It does not matter what the intent of individual Apple reps are, it matters what THE ALGORITHM theyve placed their complete faith in does. And what it does is discriminate. This is fucked up.

While Apple and its underwriters Goldman Sachs were ultimately cleared by US regulators of violating fair lending rules last year, it rekindled a wider debate around AI use across public and private industries.

Politicians in the European Union are now planning to introduce the first comprehensive global template for regulating AI, as institutions increasingly automate routine tasks in an attempt to boost efficiency and ultimately cut costs.

That legislation, known as the Artificial Intelligence Act, will have consequences beyond EU borders, and like the EUs General Data Protection Regulation, will apply to any institution, including UK banks, that serves EU customers. The impact of the act, once adopted, cannot be overstated, said Alexandru Circiumaru, European public policy lead at the Ada Lovelace Institute.

Depending on the EUs final list of high risk uses, there is an impetus to introduce strict rules around how AI is used to filter job, university or welfare applications, or in the case of lenders assess the creditworthiness of potential borrowers.

EU officials hope that with extra oversight and restrictions on the type of AI models that can be used, the rules will curb the kind of machine-based discrimination that could influence life-altering decisions such as whether you can afford a home or a student loan.

AI can be used to analyse your entire financial health including spending, saving, other debt, to arrive at a more holistic picture, Sarah Kocianski, an independent financial technology consultant said. If designed correctly, such systems can provide wider access to affordable credit.

But one of the biggest dangers is unintentional bias, in which algorithms end up denying loans or accounts to certain groups including women, migrants or people of colour.

Part of the problem is that most AI models can only learn from historical data they have been fed, meaning they will learn which kind of customer has previously been lent to and which customers have been marked as unreliable. There is a danger that they will be biased in terms of what a good borrower looks like, Kocianski said. Notably, gender and ethnicity are often found to play a part in the AIs decision-making processes based on the data it has been taught on: factors that are in no way relevant to a persons ability to repay a loan.

Furthermore, some models are designed to be blind to so-called protected characteristics, meaning they are not meant to consider the influence of gender, race, ethnicity or disability. But those AI models can still discriminate as a result of analysing other data points such as postcodes, which may correlate with historically disadvantaged groups that have never previously applied for, secured, or repaid loans or mortgages.

And in most cases, when an algorithm makes a decision, it is difficult for anyone to understand how it came to that conclusion, resulting in what is commonly referred to as black-box syndrome. It means that banks, for example, might struggle to explain what an applicant could have done differently to qualify for a loan or credit card, or whether changing an applicants gender from male to female might result in a different outcome.

Circiumaru said the AI act, which could come into effect in late 2024, would benefit tech companies that managed to develop what he called trustworthy AI models that are compliant with the new EU rules.

Darko Matovski, the chief executive and co-founder of London-headquartered AI startup causaLens, believes his firm is among them.

The startup, which publicly launched in January 2021, has already licensed its technology to the likes of asset manager Aviva, and quant trading firm Tibra, and says a number of retail banks are in the process of signing deals with the firm before the EU rules come into force.

The entrepreneur said causaLens offers a more advanced form of AI that avoids potential bias by accounting and controlling for discriminatory correlations in the data. Correlation-based models are learning the injustices from the past and theyre just replaying it into the future, Matovski said.

He believes the proliferation of so-called causal AI models like his own will lead to better outcomes for marginalised groups who may have missed out on educational and financial opportunities.

It is really hard to understand the scale of the damage already caused, because we cannot really inspect this model, he said. We dont know how many people havent gone to university because of a haywire algorithm. We dont know how many people werent able to get their mortgage because of algorithm biases. We just dont know.

Matovski said the only way to protect against potential discrimination was to use protected characteristics such as disability, gender or race as an input but guarantee that regardless of those specific inputs, the decision did not change.

He said it was a matter of ensuring AI models reflected our current social values and avoided perpetuating any racist, ableist or misogynistic decision-making from the past. Society thinks that we should treat everybody equal, no matter what gender, what their postcode is, what race they are. So then the algorithms must not only try to do it, but they must guarantee it, he said.

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While the EUs new rules are likely to be a big step in curbing machine-based bias, some experts, including those at the Ada Lovelace Institute, are pushing for consumers to have the right to complain and seek redress if they think they have been put at a disadvantage.

The risks posed by AI, especially when applied in certain specific circumstances, are real, significant and already present, Circiumaru said.

AI regulation should ensure that individuals will be appropriately protected from harm by approving or not approving uses of AI and have remedies available where approved AI systems malfunction or result in harms. We cannot pretend approved AI systems will always function perfectly and fail to prepare for the instances when they wont.

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Risks posed by AI are real: EU moves to beat the algorithms that ruin lives - The Guardian

The need for organizations to quickly create new business models and marketing channels has accelerated AI adoption throughout the past couple of years. This is especially true in healthcare, where data analytics accelerated the development of COVID-19 vaccines. In consumer-packaged goods, Harvard Business Reviewreportedthat Frito-Lay created an e-commerce platform,Snacks.com, in just 30 days.

The pandemic also accelerated AI adoption in education, as schools were forced to enable online learning overnight. And wherever possible, the world shifted to touchless transactions, completely transforming the banking industry.

Three technology developments during the pandemic accelerated AI adoption:

[ Also readArtificial Intelligence: How to stay competitive. ]

Lets look at the pros and cons of these developments for IT leaders.

Even 60 years after Moores Law, computing power is increasing, with more powerful machines and more processing power through new chips from companies like NVidia.AI Impactsreports that computing power available per dollar has probably increased by a factor of ten roughly every four years over the last quarter of a century (measured in FLOPS or MIPS). However, the rate has been slower over the past 6-8 years.

Pros: More for less

Inexpensive computing gives IT leaders more choices, enabling them to do more with less.

Cons: Too many choices can lead to wasted time and money

Consider big data. With inexpensive computing, IT pros want to wield its power. There is a desire to start ingesting and analyzing all available data, leading to better insights, analysis, and decision-making.

But if you are not careful, you could end up with massive computing power and not enough real-life business applications.

As networking, storage, and computing costs drop, the human inclination is to use them more. But they dont necessarily deliver business value to everything.

Before the pandemic, the terms data warehouses and data lakes were standard and they remain so today. But new data architectures like data fabric and data mesh were almost non-existent. Data fabric enables AI adoption because it enables enterprises to use data to maximize their value chain by automating data discovery, governance, and consumption. Organizations can provide the right data at the right time, regardless of where it resides.

Pros: IT leaders will have the opportunity to rethink data models and data governance

It provides a chance to buck the trend toward centralized data repositories or data lakes. This might mean more edge computing and data available where it is most relevant. These advancements result in appropriate data being automatically available for decisioning critical to AI operability.

Cons: Not understanding the business need

IT leaders need to understand the business and AI aspects of new data architectures. If they dont know what each part of the business needs including the kind of data and where and how it will be used they may not create the correct type of data architecture and data consumption for proper support. ITs understanding of the business needs, and the business models that go with that data architecture, will be essential.

Statistaresearch underscores the growth of data: The total amount of data created, captured, copied, and consumed globally was 64.2 zettabytes in 2020 and is projected to reach more than 180 zettabytes in 2025. Statista research from May 2022 reports, The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic. Big data sources include media, cloud, IoT, the web, and databases.

Pros: Data is powerful

Every decision and transaction can be traced back to a data source. If IT leaders can use AIOps/MLOps to zero in on data sources for analysis and decision-making, they are empowered. Proper data can deliver instant business analysis and provide deep insights for predictive analysis.

Cons: How do you know what data to use?

More on artificial intelligence

Besieged by data from IoT, edge computing, formatted and unformatted, intelligent and unintelligible IT leaders are dealing with the 80/20 rule: What are the 20 percent credible data sources that deliver 80 percent of the business value? How do you use AI/ML ops to determine the credible data sources, and what data source should be used for analysis and decision-making? Every organization needs to find answers to these questions.

AI is becoming ubiquitous, powered by new algorithms and increasingly plentiful and inexpensive computing power. AI technology has been on an evolutionary road for more than 70 years. The pandemic did not accelerate the development of AI; it accelerated its adoption.

Harnessing AI is the challenge ahead.

[ Want best practices for AI workloads? Get theeBook: Top considerations for building a production-ready AI/ML environment. ]

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Artificial Intelligence: 3 ways the pandemic accelerated its adoption - The Enterprisers Project

Its easy to get excited about AI projects. Especially when you hear about all the amazing things people are doing with AI, from conversational and natural language processing (NLP) systems, to image recognition, autonomous systems and great predictive analytics and pattern and anomaly detection capabilities. However, when people get excited about AI projects, they tend to overlook some significant red flags. And its those red flags that are causing over 80% of AI projects to fail.

One of the biggest reasons for AI project failure is that companies dont justify the use of AI from an return on investment (ROI) perspective. Simply put, theyre not worth the time and expense given the cost, complexity, and difficulty of implementing the AI systems.

Organizations rush past the exploration phase of AI adoption, jumping from simple proof-of-concept demos right to production without first assessing whether the solution will provide any positive return. One big reason for this is that measuring AI project ROI can prove more difficult than first expected. Far too often teams are getting pressure from upper management, colleagues, or external teams to just get started with their AI efforts, and projects move forward without a clear answer to the problem they are actually trying to solve or the ROI thats going to be seen. When companies struggle to develop a clear understanding of what to expect when it comes to the ROI of AI, misalignment of expectations is always the result.

Missing and Misaligned ROI Expectations

So, what happens when the ROI of an AI project isnt aligned with expectations from management? One of the most common reasons why AI projects fail is the ROI is not justified by the investment of money, resources, and time. If you're going to be spending your time, effort, human resources, and money implementing an AI system, you want to get a well-identified positive return.

Even worse than a misaligned ROI is the fact that many organizations arent even measuring or quantifying ROI to begin with. ROI can be measured in a variety of ways from a financial return such as generating income or reducing expenses, but it can also be measured as a return on time, shifting or reallocating of critical resources, improving reliability and safety, reducing errors and improving quality control, or improving security and compliance. Its easy to see how an AI project could provide a positive ROI if you spend a hundred thousand dollars on an AI project to eliminate two million dollars of potential cost or liability, then its worth every dollar spent to reduce the liability. But youll only see that ROI if you actually plan for it ahead of time and manage that ROI.

Management guru Peter Drucker once famously said, you can't manage what you don't measure. The act of measuring and managing AI ROI is what sets apart those who see positive value from AI from those who end up canceling their projects years and millions of dollars into their efforts.

Boiling the Ocean and Biting off More than You Can Chew

Another big reason why companies arent seeing the ROI they are expecting is that projects are trying to bite off way too much all at once. Iterative, agile best-practices, especially those employed by best practice AI methodologies such as CPMAI clearly advise project owners to Think Big. Start Small. Iterate Often. There are unfortunately many unsuccessful AI implementations that have taken the opposite approach by thinking big, starting big, and iterating infrequently. One case in point is Walmarts investment in AI-powered robots for inventory management. In 2017 Walmart invested in robots to scan store shelves, and by 2022 they pulled them out of stores.

Clearly Walmart had sufficient resources and smart people. So you cant blame their failure on bad people or bad technology. Rather, the main issue was a bad solution to the problem. Walmart realized that it was just cheaper and easier to use human employees they already had working in the stores to complete the same tasks the robot was supposed to do. Another example of a project not returning the expected results can be found with the various applications of the Pepper robot in supermarkets, museums, and tourist areas. Better people or better technology wouldnt have solved this problem. Rather just a better approach to managing and evaluating AI projects. Methodology, folks.

Adopting a Step-by-step approach to running AI and machine learning projects

Did these companies get caught up in the hype of the technology? Were these companies just looking to have a robot roaming the halls for the cool factor? Because being cool isnt solving any real business problems nor solving a pain point. Don't do AI for the sake of AI. If you do AI just for the sake of AI then don't be surprised when you don't have a positive ROI.

So, what can companies do to ensure positive ROI for their projects? First, stop implementing AI projects for AIs sake. Successful companies are adopting a step by step approach to running AI and machine learning projects. As mentioned earlier, methodology is often the missing secret sauce to successful AI projects. Organizations are now seeing benefit in employing approaches such as the Cognitive Project Management for AI (CPMAI) methodology, built upon decades-old data centric project approaches such as CRISP-DM and incorporating established best-practice agile approaches to provide for short, iterative sprints for projects.

These approaches all start with the business user and requirements in mind. The very first step of CRISP-DM, CPMAI, and even Agile is to figure out if you should even move forward with an AI project. These methodologies suggest alternate approaches, such as automation or straight up programming or even just more people might be more appropriate to solve the problem at hand.

The AI Go No Go Analysis

AI Go No Decisions, CPMAI Methodology, Cognilytica

If AI is the right solution then you need to make sure that you answer yes to a variety of different questions to assess if youre ready to embark on your AI project. The set of questions you need to ask to determine whether to move forward with an AI project is called the AI Go No Go analysis and this is part of the very first phase in the CPMAI methodology. The AI Go No Go analysis has users ask a series of nine questions in three general categories. In order for an AI project to actually go forward, you need three things in alignment: the business feasibility, the data feasibility, and the technology / execution feasibility. The first of the three general categories asks about the business feasibility and asks you if there is a clear problem definition, if the organization is actually willing to invest in this change once created, and if there is sufficient ROI or impact.

These may seem like very basic questions, but far too often these very simple questions are skipped. The second set of questions deals with data including data quality, data quantity, and data access considerations. The third set of questions is around implementation including whether you have the correct team and skill sets needed, can execute the model as required, and that the model can be used where planned.

The most difficult part of asking these questions is being honest with the answers. Its important to be really honest when addressing whether to move forward with the project, and if you answer no to one or more of these questions it means either you're not ready to move forward yet or you should not move forward at all. Dont just plow ahead and do it anyway because if you do, don't be surprised when youve wasted a lot of time, energy and resources and dont get the ROI you were hoping for.

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You Need To Stop Doing This On Your AI Projects - Forbes

As technological innovation continues to accelerate and artificial intelligence (AI) becomes more prevalent, businesses are looking for ways to build the next generation of AI talent. According to Gartner, over 80% of Internet of Things (IoT) activities in enterprises will be employing AI and machine learning. Skilled workers are the most important factor in AI development. Although technology and algorithms have become commoditized, there is a big demand for workers who can solve problems with AI.

Here are a few things CIOs can do in order to make this happen.

Nurture the next generation of AI talent

Nurture and grow next-gen AI talent through continuous innovation where industry, science, engineering, and human ingenuity intersect. They need to give talented AI professionals a good place to work. They need to make sure they have the freedom to create value and meet their expectations. Create tech hubs to grow your local ecosystems and build the next generation of AI talent now.

Merit to education in AI

AI workshops, certifications, and bootcamps do not have any educational merit and do not build practitioner level skills. You need to build AI education around the intellectual infrastructure that already exists in local academic communities. The centers of excellence that engage via an eight-stakeholder model must form out of those communities to make AI education effective and bring merit to education in AI. CIOs need to identify the areas where the local ecosystem is lacking and use this as an opportunity to create value. This means that the technology and academic communities in each region need to work together to build local AI centers of excellence. Education is required to lead in the field of artificial intelligence. Thats why it is so important to make sure the academic system for this discipline is good.

Support from national governments

National governments need to support AI ecosystems from the grassroot level. Each stakeholder in an AI ecosystem has a role to play in order to build a value network that goes from the local government up to a federal policymaker. An AI ecosystem is made up of eight different stakeholders, and each one has different goals. For these stakeholders to achieve their goals, they need the support of the government. National governments should recognize AI degrees and education immediately at the graduate school level.

In what seems to align with Indian Prime Minister Narendra Modis Digital India vision, Deloitte and IIT Roorkee have announced a collaboration to empower and build the next generation of Indian talent in the field of AI. Deloitte and IIT Roorkee will together deliver rigorous, immersive programs in AI and machine learning that are designed to build the next generation workforce. This will revolutionize how organizations and academia work together to overcome the AI talent gap by imparting industry-relevant skills to Indian talent in new-age tools and developing future leaders who are highly proficient in AI.

The future of AI is bright, and businesses need to start preparing now for the talent they will need in the future. By considering the suggestions weve outlined, CIOs can make sure their business is at the forefront of this exciting industry. Are you ready to build the next gen of AI talent?

Also read:Automate your work processes with Digital Employees

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How can CIOs build the next generation of AI talent? - Wire19