Category: Automation/RPA/AI

The impact of automation collapsing enterprise IT | Sherpas in Blue Shirts

The IT stack is collapsing, thanks to the latest innovation in IT and moving into a software-defined service-oriented architecture. What can happen as a result of the collapse is important for every company to understand, as the more the stack collapses, the better results IT can deliver.

Before we look at the potential impacts, let me explain what I mean by collapsing the stack. A multi-layer stack of technology comprises IT – things like the server layer, operating system, middleware, enterprise application, security layer, etc. That stack informs and drives a reciprocal stack, which is the functional organization of enterprise IT. In this functional stack are infrastructure, database, middleware software, database and middleware teams, application maintenance and development, security, etc.

 

Read More Here

Heralding the Robot Revolution in Human Resources! | Sherpas in Blue Shirts

Until a year or so ago, the common refrain among those operating in the HR function was that HR services were already so heavy with platform automation that there wasn’t much that Robotic Process Automation (RPA) or Artificial Intelligence, its more advanced cousin, could do.

However, my extensive research shows that HR has enthusiastically jumped on the bandwagon. Even though many automation projects still inhabit the realm of slideware, many HR leaders and their service provider brethren are recognizing the impacts that automation can have on both their costs and revenue.

Enterprises: cost impact

Even though Human Resource services are heavy with platform automation (these platforms can be the traditional ones such as SAP and PeopleSoft or the new-age ones such as Workday and SuccessFactors), humans continue to do transactional tasks, such as entering data into platforms, transferring data between platforms, or preparing templated documents such as offer letters. RPA can perform these types of tasks faster and more accurately, and leave a reliable audit trail, wherever needed. It also frees people to do higher-order work. The result is significantly better efficiency and reduced costs.

Enterprises: revenue impact

Automation can also boost revenue indirectly by enhancing the employee experience, which in turn increases productivity. Think chatbots. While the current chatbot implementations are mostly RPAs, infusion of AI features such as Natural Language Processing (NLP), machine learning, and conversational user interfaces can be game changing. For instance, AI-enabled chatbots could remove employees’ toil from applying for leaves, filing expense reports or timesheets, selecting benefit plans, or getting answers to common HR queries. Managers could use chatbots to help shortlisted candidates, provide personalized onboarding assistance, and collect performance evaluation feedback. Chatbots’ 24/7 availability, ease of use, and rapid and accurate responses contribute directly to better productivity and experience.

HR service providers: cost impact

Service providers too are gaining significant cost and efficiency benefits through RPA, which mainly translates to deploying less Full-Time Employees (FTEs) to deliver the same outputs. Providers are presently grabbing most of these cost benefits to expand their margins, rather than pass them on to their clients in the form of reduced prices. That approach works like a charm for providers because the dominant output-based pricing model (per employee served, per pay slip processed, etc.) of HR services delinks FTE count from pricing, thus hiding gains through FTE reduction from clients. That is unlike the case of say, an F&A services construct, where the pricing is usually input-based (per FTE) and enterprise customers pressure providers to reduce FTE count through RPA and thus, cut prices.

HR service providers: revenue impact

However, enterprises are steadily wising up to RPA benefits that can drive lower price. Moreover, with increasing competition, providers are increasingly using RPA to set ever lower prices. Thus, providers will soon be forced to make a choice – do nothing and let others take away their business, or aggressively deploy RPA and cannibalize themselves but retain clients. The latter is obviously the lesser evil, even though revenues will be adversely impacted. That is when providers that look beyond RPA and invest in AI-based automation will trump the market. AI-based automation can provide benefits, such as enhanced employee productivity, for which enterprises will be willing to pay a premium. Powerful AI-based automation can also help providers deliver services they were earlier incapable of delivering, such as cognitive analytics. That can expand revenues and counter the cannibalization effect of RPA.

Clearly, automation is steadily becoming an imperative for both enterprises and HR services providers.

However, as with any advanced technology, enthusiasm without a generous helping of caution can be a dangerous potion. Setting realistic expectations about the benefits of automation, investing in technological and cultural change management, and bringing on-board key stakeholders are a few keys to success for enterprises and service providers in the long journey that is an automation implementation.

Keep your eyes peeled for my drill-down blog on these, and other, keys to success! In the meantime, feel free to share your opinions and stories of automation in HR – why or why not go for it, what works and what doesn’t, etc., directly with me at [email protected].

Why Invest in Artificial Intelligence (AI)? | Sherpas in Blue Shirts

“Facebook shuts down robots after they invent their own language.” This headline was splashed across myriad news outlets just a few weeks ago. And although the story itself made the event seem like just a normal science experiment, this type of alarming tone in media reports is becoming the norm and is sowing seeds of doubt, fear, and uncertainty among consumers and even some businesses.

However, behind the vendor hype and the media fear mongering, there are real, bona fide reasons for organizations to invest in artificial intelligence (AI).

Humans can perform various expert tasks with relevant training and experience. For example, a research analyst trained for and with experience in market research, can predict future market size and growth with considerable accuracy. Using machine learning, a system can be trained to perform the same task. Yet, with their enormous computational power, such expert systems/machines can beat humans’ speed, accuracy, and efficiency in this and many other tasks. This is the reason why many organizations are investing heavily in developing and creating AI-enabled systems.

Narrow AI

Have you ever encountered a situation where you’re talking to a customer service executive over chat, and wondered if you’re actually talking to a real human agent or a virtual agent/computer program?

I recently attended IPsoft’s Amelia 3.0 launch event. Amelia is an AI-powered virtual agent platform that uses advanced machine learning and deep learning techniques to get progressively better at performing tasks. In one of the more interesting demonstrations, Amelia went head-to-head with a real person in answering questions posed to it in natural language, by real-time processing of unstructured data from natural language documents such as Wikipedia pages. It was fascinating to see how Amelia could answer questions with considerable accuracy.

Such domain-specific expert systems that can simulate human-like capacities and even outperform human expertise in specific domains are called Narrow AI.

While most AI vendors typically focus on building Narrow AI systems for a specific purpose such as virtual agent capabilities, some large vendors such as IBM, under its Watson brand, offers multiple individual Narrow AI systems to cover a wide range of use cases.  For example, it is being used at several top cancer hospitals in the U.S. to help with cancer research by speeding up DNA analysis in cancer patients. In the finance sector, DBS bank in Singapore uses Watson to ensure proper advice and experience for customers of its wealth management business. And in retail, an online travel company has created a Discovery Engine that uses Watson to take in and analyze data to better link additional offers and customize preferences for individual consumers.

True, or General, AI

Artificial intelligence with multiple and broader capabilities is called True, or General, AI. When it comes to developing General AI, which has the ability to generalize and apply learnings to unlimited new domains or unexpected situations – something that humans often do – I think we are just scratching the surface. Primary barriers to achieving General AI are our lack of understanding of everything happening inside human brain and the technical feasibility of creating a system as sophisticated, complex, and vast as the human brain. As per a survey of 352 researchers published in 2017, there is a 50 percent probability that General AI will happen by around the year 2060.

Current lay of the land – A world of opportunities

Despite the many evolutional, ethical, and developmental challenges researchers and technology developers continue to face in making artificial intelligence more capable and powerful, I believe that even existing AI technology presents unique opportunities for organizations. It enables them to improve the customer experience and operational efficiency, enhance employee productivity, cut costs, accelerate speed-to-market, and develop more sophisticated products.

To help its clients understand the AI technology market better, Everest Group is researching this field with a lens on global services. Although early in our research, one fascinating use case is how AI is automating decision making with complete audit trail in the heavily regulated financial services industry. The research will be published in October, 2017 as part of our research program, Service Optimization Technologies (SOT), that focuses on technologies that are disrupting the global services space.

An Outsider’s Inside View of the Global Services Industry: New Value Props, and Bots to Boot | Sherpas in Blue Shirts

Just a month ago I rejoined Everest Group as its chief research guru. And while I thoroughly enjoyed my stints as chief research officer at Market Track (a competitive intelligence firm for advertisers) and The Hackett Group (an intellectual property-based strategic consultancy and benchmarking firm) over the last 10 years, I’m feeling like a kid in a candy store in today’s digitally-oriented global services industry!

Here are my gut reactions to visits I had last week with two sell-side organizations.

Wipro

Wow, wow, wow.

That’s research speak for how I felt after the inauguration of Wipro’s brand new Silicon Valley Innovation Center on August 1. The Center, which Wipro bills as, “…state-of-the-art R&D and incubation hub, designed to develop and showcase next-generation technologies and solutions for enterprises” clearly displayed how much its value proposition has changed.

It wasn’t that long ago that Wipro and its peers were promoting savings, quality, and scale, along with a thin layer of industry expertise. Now it’s showcasing innovative solutions along a broad array of concepts that include the future of retail, banking, and healthcare, to name just a few.

It’s clear Wipro knows that the robots are coming, rendering its traditional proposition passé, similar to what EDS, CSC, ACS, and HPE experienced over the past 15 or so years. So will its ideas be enough to compete in this dog-eat-digital global services environment? It’s hard to say, but it’s certainly going to give it the old college try. We’ll update our thoughts in due time.

Automation Anywhere

No C3POs to be found, but I did see some game changers.

I took advantage of my time in Silicon Valley to stop by Automation Anywhere’s headquarters. And I was sorely disappointed when they didn’t show me a warehouse full of R2D2 and C3PO robots. Instead, they showed me an evolutionary capability that has reached a tipping point that should make enterprise executives do an immediate rethink of how they design their organizations.

I had a spirited debate with CEO Mihir Shukla and his team about how Automation Anywhere’s RPA-based solution will impact enterprises. Our mutual thoughts were that some will use it incrementally to create short-term savings and process improvements, but that really innovative executives will use it as one of several key tools to change the competitive landscape in their markets. For them, it will be a thing of beauty. For others? Well, let’s be positive.

Watch this space for some really cool fact-based insights that help differentiate the winner and loser enterprises over the coming months.

RPA and BPM – The Twain Shall Meet after All | Sherpas in Blue Shirts

It was not long ago that I was talking to a German manufacturer about the relative merits of different types of automation solutions on the market. The client did not want any services-layer, API or connector-based process integration. He said those were in the realm of Business Process Management (BPM) and IT. That is why he was going for Robotic Process Automation (RPA), for integration through the user interface. We discussed the pros and cons of these different approaches – but the point is that he was looking for an alternative to traditional BPM. He, and many others, have come to view RPA as that alternative. Yet, recent announcements by leading vendors show that RPA and BPM are coming together. Announcements by IBM and Automation Anywhere, and Appian and Blue Prism, indicate that we have really come full circle and that the RPA and BPM twain have already met.

This was inevitable:

  • The recent success of RPA was a bolt out of the blue for the BPM market, distracting and taking away many potential customers and reaching business users that BPM providers could only dream about. BPM vendors had to take steps to protect their share of the market
  • The growing scale of RPA deployments is another driver for the twain to meet. It is one thing having a few robots running basic processes, but as organizations’ automation ambitions have become loftier, the need for integration with workflow to increase control and orchestration has grown too
  • Robotic Process Automation is not the answer to all automation requirements and, therefore, combining it with BPM for a full set of capabilities to handle different situations is a no brainer. Some of the most successful automation deployments combine RPA with BPM-based large strategic system integration and transformation. In these scenarios, RPA complements the BPM integration by connecting core business platforms to other disparate enterprise systems

IBM and Automation Anywhere

With their announcement, IBM and Automation Anywhere have taken their partnership to the deeper level of integrated offerings:

  • IBM will include Automation Anywhere Enterprise edition in its BPM software catalogue. Currently BotFarm and Automation Anywhere’s cloud offerings are not included
  • It will resell and support Automation Anywhere
  • IBM will integrate Automation Anywhere with the software becoming a part of its IBM Digital Process Automation platform. This included IBM Business Process Manager (BPM) and IBM Operational Decision Manager (ODM)
  • Automation Anywhere will be the standard RPA software offering unless clients ask for another

As things stand today, IBM Digital Process Automation orchestrates processes between core systems while Automation Anywhere RPA automates repetitive rules-based tasks. IBM’s vision for the future is that BPM and RPA will be integrated into a flexible offering with software, services and consultancy provided from a single source. In the future, we will see IBM add cognitive capabilities to this mix. The question is how much of Automation Anywhere’s intelligent capabilities will feature in IBM’s software catalogue.

While the move by IBM to build this partnership is part of the maturing RPA market, it must have been partly driven by a move by its other major RPA partner, Blue Prism, to join forces with Appian, a BPM vendor with whom Blue Prism has built deeper software integration. Last year, another BPM player, Pega, acquired Open Span, which also offers Robotic Process Automation.

An additional driver is that RPA offers integration at a relatively low cost of entry, and this partnership allows IBM to bring in customers at a lower starting point to traditional BPM projects.

Appian and Blue Prism

This week, Appian and Blue Prism, which, had already built some plug and play capabilities together, took their relationship to the next level with the announcement of an extension to Appian’s platform that is based on RPA from Blue Prism. The partners are also aiming for a one-stop-shop to all automation requirements and seamless integration between their combined BPM and RPA products.

Interestingly, the Blue Prism partnership with IBM is going on, unaffected by these pairings. The groups involved are different: Blue Prism started in IBM’s Global Process Services and expanded to GBS Digital. Automation Anywhere’s relationship is with IBM Software.

It is important to note that the Appian move is part of Blue Prism’s strategy to turn its software into a platform that other solutions can be plugged into easily. The Blue Prism Technology Alliance Program (TAP) will see integrated offerings from partners in cloud, virtualization, analytics, process mining, artificial intelligence including computer vision, as well as BPM. IBM is a TAP partner. Others, include Celaton and Instream, its intelligent automation software.
These alliances open new opportunities for Blue Prism, for example, to handle processes that use unstructured content and to access services that are run on cloud solutions. In summary, interoperability is going to be a key feature of the Blue Prism platform and the Appian move is a major step in that direction.

Everest Group has addressed aspects of automating different levels of processes with different solution types in a paper titled “Pushing the Dial on Business Process Automation”.

Everest Group has just positioned IBM as a Leader in a PEAK Matrix™ assessment of Business Process Services Delivery Automation (BPSDA).

I Can’t Get No Satisfaction – The CSAT View of RPA in BPS Contracts | Sherpas in Blue Shirts

Everest Group’s latest research shows that while there is growing adoption of Robotic Process Automation (RPA) within Business Process Services (BPS) contracts, customer satisfaction (CSAT) with service providers’ is distinctly average at 3.6 out of 5. Service providers need to do more to achieve better CSATs.

Everest Group’s research titled Business Process Services Delivery Automation (BPSDA) – Service Provider Landscape with PEAK Matrix™ Assessment 2017, which assesses the automation capabilities of leading service providers, shows that the number of automation proofs of concept run by service providers for their BPS clients has on average quadrupled in one year. Furthermore, the number of BPS contracts with automation has gone above 1,000. Yet, the scale of deployments is small; the average number of robots deployed per BPS client hovers at just below 10.

Unsurprisingly, at 85%, the vast majority of deployments are robotic process automation as opposed to automation based on Artificial Intelligence (AI).

The other finding from the report, based on interviews with reference clients of the service providers shows that CSAT with service providers automation services is fair to middling. The average overall score was 3.6 out of 5. Clients rated the need for RPA skills very highly with service providers achieving 3.9 for their RPA related services. The scores were dragged down by the CSAT for AI capabilities that scored only 3.1.

The kind of issues that the reference clients reported were mainly related to the immaturity of RPA in the global services market and the skills for deploying it. Examples of feedback include:

  • It feels like they used us as a training arena for some of their staff
  • They (the BPS provider) should communicate opportunities better. It feels like they were late to bring this to us
  • They took a long time to learn how to code in xyz RPA software. It took a long time to integrate the RPA with our systems
  • The service provider should have done more due diligence on the RPA technology vendor
  • Change management and governance need improving

What can we expect with Robotic Process Automation?

Some of this dissatisfaction is a result of the hype in the market about the ease of robotic process automation deployments and rapid returns on investment. Clients have high expectations from all RPA projects, and this is showing in projects that they deploy for themselves or through system integrators as well.

The good news is that service providers continue to invest in automation. On average, 60% of service providers’ technology staff are working on automation products, solutioning and related services. This will enhance their skills and capabilities in SDA technologies, both RPA and AI.

We expect to see skills grow alongside the market in the coming months. We’ll be watching this space closely to provide our clients with updates over the year.

Explainable AI? Why Should Humans Decide Robots are Right or Wrong? | Sherpas in Blue Shirts

I have been a vocal proponent of making artificial intelligence (AI) systems more white box – able to “explain” why and how they came to a particular decision – than they are today. Therefore, I am heartened to see increasing debate around this. For example, Eric Brynjolfsson and Andrew McAfee, the well-known authors of the book, “The Second Machine Age,” have increasingly spoken about this.

However, sometimes I debate with myself on various aspects of explainable AI.

What are we explaining? If we have a white box AI system, a human can understand “why” the system made a certain decision. But who decides whether or not the decision was correct? For example, in the movie “I, Robot,” the lead character (played by actor Will Smith) thought the humanoid robot should have saved a child instead of him. Was the robot wrong? The robot certainly did not think so. If humans make the “right or wrong” decision for robots, doesn’t that defeat the self-learning purpose of AI?

Why are we explaining? Humans seek explanation when they lack understanding of something or confidence in the capabilities of other humans. Similarly, we seek explanation from artificial intelligence because, at some level, we aren’t sure about the capabilities of these systems. (Of course, there’s also humans’ control freak characteristic.) Why? Because these are “systems” and systems have problems. But humans also have “problems,” and what each individual person considers “right” is defined by their own value system, surroundings, and situations. What’s right for one person may be wrong for another. Should this contextual “rightness or wrongness” also apply to AI systems?

Who are we explaining? Should an AI system’s outcome be analyzed by humans or by another AI system? Why do we believe we have what it takes to assess the outcome, and who gave us the right to do so? Can we become more trustworthy, and believe that an AI system can assess another? Perhaps, but this defeats the very debate on explainable AI.

Who do we hold responsible for the decisions made by Artificial Intelligence?

The mother lode of complexity here is around responsibility. Due to human’s beliefs that AI systems won’t be “responsible” in their decisions – based on individuals’ biased definition of what is right or wrong – regulations are being developed to hold humans responsible for the decisions of AI systems. Of course, these humans will want an explanation from the AI system.

Regardless of which side of the argument you are on, or even if you pivot daily, there’s one thing I believe we can agree on…if we end up making better machines than better humans through artificial intelligence, we have defeated ourselves.

Voice-enabled Enterprise Applications: NOT a Good Idea | Sherpas in Blue Shirts

With the increasing proliferation of voice-enabled personal digital assistants (e.g., Cortana, Google Assistant, Samsung’s Bixby, and Siri) enterprise application vendors are considering jumping into the fray. In fact, some vendors have gone so far as to believe that, in the near future, 90 percent of interactions with their enterprise applications will be through voice or digital assistants.

But would these vendors actually be solving a real business problem with these capabilities, or perhaps instead getting intoxicated by drinking their own Kool-aid?

True that there are many potent arguments for voice-enabled interaction, including elimination of the need to train users on how to operate a given application, and the implication of higher productivity as the volume of data a person needs to physically enter into a system is greatly reduced.

But consider the realities of the user experience. Picture this: you’re sitting in your office area writing a report for a customer, when all of a sudden you hear your colleague saying to his laptop or smart phone, “OK SAP/Oracle, create an invoice.” How disruptive would this be to your work? Vendors could potentially tune their applications to such frequency that, when coupled with an additional device, a user’s speech couldn’t be heard by others. But that sounds too cumbersome and meaningless.

So what’s the right enterprise application vision for vendors?

The vision vendors should drive toward is one of no interaction between users and the enterprise application. Given that people engage with enterprise applications because they “have” to, not because they “want” to, how about a future where user activities are tracked, and all the related processes execute on their own?

For example, what if an enterprise’s system could manage all aspects of its employees’ travel expenses, rather than each individual filing expenses, and an army of staff reconciling and paying them? Such a nirvana of automated business processes would have tremendous impact on business agility, cost savings, and the user experience.

Voice assistance could potentially be of value in the back-end of enterprises’ systems. Their support staff could get a tremendous boost if they could “speak-fix” a problem instead of debugging complex code every time something went wrong. System designers and builders might also derive some value from voice interactions.

Enterprise applications vendors need to carefully consider whether they are solving the right problem with voice enablement. Could their smart and expensive developers be deployed elsewhere to solve complex and pertinent business problems, rather than creating a potentially unnecessary user experience? I believe that, at the end of the day, voice enablement is likely not the right, broad-based overhaul for the user experience with enterprise applications. Vendors need to focus on what users need, rather than getting caught up in the fancy of using the latest shiny digital toys.

Reimagining Global Services: How to get MORE out of Technology | Sherpas in Blue Shirts

Much has been written and said about the Bimodal IT model Gartner introduced in 2014 – with forceful arguments for and against. Not at all intending to bash that model, it’s safe to say that the digital explosion over the last three years demands that enterprises’ technology strategies be much more nuanced and dynamic.

The MORE model for global services

Let me explain with the help of the following chart. I call it the Maintain-Optimize-Reimagine-Explore – the MORE – model.

Global Services and Technology in the MORE model

I’ve tried to plot (intuitively) a bunch of technology and service themes on their current and future innovation potential.

  • Maintain: On the bottom right are themes like mainframes and traditional hosting services that are unlikely to go through dramatic changes in the near term. These are exceptionally stable and commoditized, and will not attract exciting investments. Enterprises still need them, and CIOs should Maintain status quo because it’s too risky and/or expensive to modernize them.
  • Optimize: Seven years back, that cool AWS deployment was the craziest, riskiest, hippest tech thing we could do. But, I guess we’ve all aged (just a little bit) since then. The needle of cloud investment for most enterprises has moved from AWS migration (USD$200 per application, anyone?) to effective orchestration and management – a clear case of the enterprise seeking to Optimize its investments in the bottom right corner of my diagram.
  • Explore: On the top right, we have the new wild, wild, west of the tech world. Blockchain can completely transform how the world fundamentally conducts commerce, IoT is working up steam, and artificial intelligence can shape a different version of human existence, much less business models. Enterprises need to Explore these to stay relevant in the future.
  • Reimagine: What we cannot afford to miss out on is the exciting opportunity to Reimagine “traditional” global services into leaner and more effective models using a combination of enabling themes like automation, DevOps, and analytics. These are immediate opportunities that many enterprises consider essential to running effective operations in a traditional AND a digital world. For example:
    • In a world where “the app is the business,” QA is being reimagined as an ecosystem-driven, as-a-service play built on extensive automation and process platforms. The reimagined QA assures a digital business process and a digital experience – not just an app.
    • We are getting into the third generation of workplace services (first hardware-centric, then operations-centric, and now software and experience-centric.) The reimagined workplace service model delivers a highly contextual, user-aware experience, without sacrificing the long-range efficiency benefits.
    • Application management services (AMS) are being reimagined through extensive outcome modeling, automation instrumentation, and continuous monitoring.

Three principles for reimagining global services

It’s interesting to note that many of these reimagination exercises are based on three common foundational principles:

  1. Automation first: Automation and intelligence lie at the heart of our ability to reimagine technology services, because automation helps us deliver breakthrough outcomes without blowing the cost model out of the water.
  2. Speed first: The need to run ALL of IT at speed is driving reimagination and the corresponding investments. If you’re at the reimagination table, throw away your tools to build the perfect (and the biggest) mousetrap. A big part of the drive for reimagination is to move from scale-driven arbitrage first models to speed-driven digital first models.
  3. Alignment always: This is important and good news. For decades, we’ve all complained about the absence of Business IT alignment. Reimagination hits out at this issue by focusing on technology architecture that is open and scalable, and by delivering as-a-service consumption models that are closely linked to things that the business really cares about.

Over the next several months, Everest Group is going to publish viewpoints on each of these topics and more. But we’d love to hear any comments and questions you have right now. Please share with us and our readers!

Software Eats World, AI Eats Software … Ethics Eats AI? | Sherpas in Blue Shirts

Marc Andreessen’s famous quote about software eating the world popped up often in the last couple of years. However, the fashionable and fickle technology industry is now relying on artificial intelligence to drive similar interest. Most people following AI would agree that there is a tremendous value society can derive from the technology. AI will impact most of our lives in more ways than we can think of today. In fact, it often is hard to argue against the value AI can potentially create for society. Indeed, with the increasing noise and real development around AI, there are murmurs that AI may replace software as the default engagement model.

Artificial intelligence may replace software

Think about it. When we use our phone or Amazon Alexa to do a voice search, we simply speak, hardly using the app or software in the traditional sense. A chatbot can become a single interface for multiple software programs that allow us to pay our electric, phone, and credit card bills.

Therefore, artificial intelligence replacing software as the next technology shift is quite possible. However, can we rely on AI? Or, more precisely, can we always rely upon it? A particularly concerning issue is that of bias. Indeed, there have been multiple debates around the bias an AI system can introduce.

But can AI be unbiased?

It’s true that humans have biases. As a result, we’ve established checks and balances, such as superiors and laws, to discover and mitigate them. But how would an AI system determine if the answer it is providing is neutral and bereft of bias? It can’t, and because of their extreme complexity, it’s almost impossible to explain why and how an AI system arrived at a particular decision or conclusion. For example, a couple of years ago Google’s algorithms classified people of a certain demography in a derogatory manner.

It is certainly possible that the people who design AI systems may introduce their own biases into them. Worse, however, is that AI systems may over a period of time start developing their own biases. And even worse, they cannot even be questioned or “retaught” the correct way to arrive at a conclusion.

AI and ethics

There have already been instances in which AI systems gave results for which they weren’t even designed. Now think about this in a business environment. For example, many enterprises will leverage an AI system to screen the resumes of potential candidates. How can the businesses be sure their system isn’t rejecting good candidates due to some machine bias?

A case of this type could be considered an acceptable, genuine mistake, and it could be argued that the system isn’t doing it deliberately. However, what happens if these mistakes eventually turn into unethicality? We can pardon mistakes but we shouldn’t do the same with unethical decisions. Taking it one step further, given that these systems ideally learn on their own, will their unethicality become manifold as the time progress?

How far-fetched it is that the AI systems become so habitually unethical that users become frustrated? What are the chances that humanity stops further developing AI systems when it realizes that it’s not possible to create AI systems without biases? While every technology brings a level of evil with the good, AI’s negative aspects could multiply very fast, and mostly without explanation. If these apprehensions scare developers away, society and business could lose AI’s tremendous potential positive improvements. That would be even more unfortunate.

As the adoption of AI systems increases, we will likely witness more cases of wrong or unethical behavior. This will fundamentally question and push regulators and developers to put boundaries around these systems. But therein is a paradox: developing systems that learn on their own, while putting boundaries around that learning – quite a contradiction. However, we must overcome these challenges to exploit the true potential of AI.

What do you think?

Have a question?

Please let us know how we can help you.

Contact us

Email us

How can we engage?

Please let us know how we can help you on your journey.