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Developing a Practical Model for Ethical AI in the Business World: Stage I – Executive Design

In this blog post series, Amalgam Insights is providing a practical model for businesses to plan the ethical governance of their AI projects. To read the introduction, click here.

This blog focuses on Executive Design, the first of the Three Keys to Ethical AI introduced in the last blog.

Stage I: Executive Design

As a starting point, any AI project needs to be analyzed in context of five major questions that are important both from a project management and scoping perspective. Amalgam Insights cannot control the ethical governance of every company, but we can provide a starting point to let AI-focused companies know what potential problems they face. As a starting point, businesses seeking to pursue ethical AI must consider the following questions:

  • What is the goal of the project?
  • What are the key ethical assumptions and biases?
  • Who are the stakeholders?
  • How will AI oversight be performed in an organization?
  • Where is the money coming from?

What is the project goal?

In thinking about the goal of the project, the project champion needs to make sure that the goal, itself, is not unethical. For instance, the high-level idea of understanding your customers is laudable at its surface. But if the goal of the project is effectively to stalk customers or to open up customer data without their direct consent, this project quickly becomes unethical. Likewise, if an AI project to improve productivity and efficiency is practically designed to circumvent legal governance of a process, there are likely ethical issues as well.

Although this analysis seems obvious, the potential opacity, complexity, and velocity of AI deployments mean that these topics have to be considered prior to project deployment. These tradeoffs need to be analyzed based on the risk profile and ethical policies of the company and need to be determined at a high level prior to pursuing an AI project.

What are the key ethical assumptions and biases?

Every AI project has ethical assumptions, compromises, and biases.

Let me repeat that.

Every AI project has ethical assumptions, compromises, and biases.

This is just a basic premise that every project faces. But because of the complexities of AI projects, the assumptions made during scoping can be ignored or minimized during the analysis or deployment if companies do not make a concerted effort to hold onto basic project tenants.

For instance, it’s easy to say that a company should not stalk its customers. And in the scoping process, this may mean masking personal information such as names and addresses from any aggregate data. But what happens if the analysis ends up tracking latitude and longitude to within 1 meter, tracking interactions every 10 minutes, and taking ethnic, gender, sexuality, or other potentially identifying or biasing data along with a phone IMEI identification into account as part of an analysis of the propensity to buy? And these characteristics are not taken into account because they weren’t included as part of the initial scoping process and there was no overarching reminder to not stalk or overly track customers? In this case, even without traditional personally identifiable information, the net result is potentially even more invasive. And with the broad scope of analysis conducted by machine learning algorithms, it can be hard to fully control the potential parameters involved, especially in the early and experimental stages of model building and recursive or neurally designed optimization.

So, from a practical perspective, companies need to create an initial set of business tenets that need to be followed throughout the design, development, and deployment of AI. Although each set of stakeholders across the AI development process will have different means of interpreting and managing these tenets, these business guidelines provide an important set of goalposts and boundaries for defining the scope of the AI project. For instance, a company might set as a set of characteristics for a project:

  • This project will not discriminate based on gender
  • This project will not discriminate based on race
  • This project will not discriminate based on income
  • This project will not take personally identifiable information without first describing this to the user in plain English (or language of development)

These tenets and parameters should each be listed separately, meaning there shouldn’t be a legalese laundry list saying “this project respects race, class, gender, sexuality, income, geography, culture, religion, legal status, physical disability, dietary restrictions, etc.” This allows each key tenet to be clearly defined based on its own merit.

These tenets should be a part of every meeting and formal documentation so that stakeholders across executive, technical, and operational responsibilities all see this list and consider this list in their own activities. This is important because each set of stakeholders will execute differently on these tenets based on their practical responsibilities. Executives will place corporate governance and resources in place while technical stakeholders will focus on the potential bias and issues within the data and algorithmic logic and operational stakeholders will focus on delivery, access, lineage, and other line-of-business concerns associated with front-line usage.

And this list of tenets needs to be short enough to be actionable. This is not the place to write a 3,000 word legal document on every potential risk and problem, but a place to describe specific high-level concerns around bias.

Who are the stakeholders?

The makeup of the executive business stakeholders is an important starting point for determining the biases of the AI project. It is important for any AI project with significant potential organizational impact to have true executive sponsorship from someone who has responsibility for the health of the company. Otherwise, it is too easy for an algorithm to “go rogue” or become an implicit and accepted business enabler without sufficient due diligence.

How will AI oversight be performed in an organization?

AI projects need to be treated with the same types of oversight as hiring employees or any significant change management process. Ideally, AI will be either providing a new and previously unknown insight or supporting productivity that will replace or augment millions of dollars in labor. Companies putting AI into place need to hold AI logic to the same standards as they would hold human labor.

Where is the money coming from?

No matter what the end goal of the AI project is, it will always be judged in context of the money used to fund the AI. If an organization is fully funding an AI project, it will be held accountable for the outcomes of the AI. If an AI project is funded by a consortium of funders, the ethical background of each funder or purchaser will eventually be considered in determining the ethical nature of the AI. Because of this, it is not enough for an organization to be pursuing an AI initiative that is potentially helpful. Organizations must also partner with or work with partners that align with the organization’s policy and culture. When an AI project becomes public, compliance officers and critics will always follow the money and use this as a starting point to determine how ethical the AI effort is.

In our next blog, we will explore Technical Development with a focus on the key questions that technical users such as data analysts and data scientists must consider as they build out the architecture and models that will make up the actual AI application or service.

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Developing a Practical Model for Ethical AI in the Business World: Introduction

As we head into 2020, the concept of “AI (Artificial Intelligence) for Good” is becoming an increasingly common phrase. Individuals and organizations with AI skillsets (including data management, data integration, statistical analysis, machine learning, algorithmic model development, and application deployment skills) have effort into pursuing ethical AI efforts.

Amalgam Insights believes that these efforts have largely been piecemeal and inadequate to meet common-sense definitions for companies to effectively state that they are pursuing, documenting, and practicing true ethical AI because of the breadth and potential repercussions of AI on business outcomes. This is not due to a lack of interest, but based on a couple of key considerations. First, AI is a relatively new capability in the enterprise IT portfolio that often lacks formal practices and guidelines and has been managed as a “skunkworks” or experimental project. Second, businesses have not seen AI as a business practice, but as a purely technical practice and made a number of assumptions in skipping to the technical development that would typically not have been made for more mature technical capabilities and projects.

In the past, Amalgam Insights has provided frameworks to help organizations take the next step to AI through our BI to AI progression.

Figure 1: Amalgam’s Framework from BI to AI




To pursue a more ethical model of AI, Amalgam Insights believes that AI efforts need to be analyzed through three key lenses:

  • Executive Design
  • Technical Development
  • Operational Deployment

Figure 2: Amalgam’s Three Key Areas for Ethical AI

In each of these areas, businesses must ask the right questions and adequately prepare for the deployment of ethical AI. In this framework, AI is not just a set of machine learning algorithms to be utilized, but an enabler to effectively augment problem-solving for appropriate challenges.

Over the next week, Amalgam Insights will explore 12 areas of bias across these three categories with the goal of developing a straightforward framework that companies can use to guide their AI initiatives and take a structured approach to enforcing a consistent set of ethical guidelines to support governance across the executive, technical, and operational aspects of initiating, developing, and deploying AI.

In our next blog, we will explore Executive Design with a focus on the five key questions that an executive must consider as they start considering the use of AI within their enterprise.

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From #KubeCon, Three Things Happening with the Kubernetes Market

This year’s KubeCon+CloudNativeCon was, to say the least, an experience. Normally sunny San Diego treated conference-goers to torrential downpours. The unusual weather turned the block party event into a bit of a sog. My shoes are still drying out. The record crowds – this year’s attendance was 12,000 up from last year’s 8000 in Seattle – made navigating the show floor a challenge for many attendees.

Despite the weather and the crowds, this was an exciting KubeCon+CloudNativeCon. On display was the maturation of the Kubernetes and container market. Both the technology and the best practices discussions were less about “what is Kubernetes” and, instead more about “how does this fit into my architecture?” and “how enterprise-ready is this stuff?” This shift from the “what” to the “how” is a sign that Kubernetes is heading quickly to the mainstream. There are other indicators at Kubecon+CloudNativeCon that, to me, show Kubernetes maturing into a real enterprise technology.

First, the makeup of the Kubernetes community is clearly changing. Two years ago, almost every company at KubeCon+CloudNativeCon was some form of digital forward company like Lyft or cloud technology vendor such as Google or Red Hat. Now, there are many more traditional companies on both the IT and vendor side. Vendors such as HPE, Oracle, Intel, and Microsoft, mainstays of technology for the past 30 years, are here in force. Industries like telecommunications (drawn by the promise of edge computing), finance, manufacturing, and retail are much more visible than they were just a short time ago. While microservices and Kubernetes are not yet as widely deployed as more traditional n-Tier architectures and classic middleware, the mainstream is clearly interested.

Another indicator of the changes in the Kubernetes space is the prominence of security in the community. Not only are there more vendors than ever, but we are seeing more keynote time given to security practices. Security is, of course, a major component of making Kubernetes enterprise-ready. Without solid security practices and technology, Kubernetes will never be acceptable to a broad swatch of large to mid-sized businesses. That said, there is still so much more that needs to be done with Kubernetes security. The good news is that the community is working on it.

Finally, there is clearly more attention being paid to operating Kubernetes in a production environment. That’s most evident in the proliferation of tracing and logging technology, from both new and older companies, that were on display on the show floor and mainstage. Policy management was also an important area of discussion at the conference. These are all examples of the type of infrastructure that Operations teams will need to manage Kubernetes at scale and a sign that the community is thinking seriously about what happens after deployment.

It certainly helps that a lot of basic issues with Kubernetes have been solved but there is still more work to do. There are difficult challenges that need attention. How to migrate existing stateful apps originally written in Java and based on n-Tier architectures is still mostly an open question. Storage is another area that needs more innovation, though there’s serious work underway in that space. Despite the need for continued work, the progress seen at KubeCon+CloudNativeCon NA 2019 point to future where Kubernetes is a major platform for enterprise applications.  2020 will be another pivotal year for Kubernetes, containers, and microservices architectures. It may even be the year of mainstream adoption. We’ll be watching.

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TEM Market Leaders Calero and MDSL Merge as Global IT Spend Management Consolidation Continues

Key Stakeholders: Chief Information Officer, Chief Financial Officer, Chief Accounting Officer, Controllers, IT Directors and Managers, Enterprise Mobility Directors and Managers, Networking Directors and Managers, Software Asset Directors and Managers, Cloud Service Directors and Managers, and other technology budget holders responsible for telecom, network, mobility, SaaS, IaaS, and IT asset and service expenses.

Why It Matters: The race for IT spend management consolidation continues. The financial management of IT is increasingly seen as a strategic advantage for managing the digital supply chain across network, telecom, wireless, cloud, software, and service portfolios.

Top Takeaway: The new combined business with over 800 employees, 3,500 customers, and an estimated 2 million devices and $20 billion under management both serves as legitimate competition for market leader Tangoe and an attractive potential acquisition for larger IT management vendors.

[Disclaimer: Amalgam Insights has worked with Calero and MDSL. Amalgam Insights has provided end-user inquiries to both Calero and MDSL customers. Amalgam Insights has provided consulting services to investors and advisors involved in this acquisition.]
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