I recently read Warren Buffett’s authorized biography The Snowball. It was a chewy read, at just under 900 pages with copious footnotes and fine grained details about what he ate, parties he attended, and vacations he took, in addition to background profiles of many of the companies he bought and larger-than-life personalities he associated with.
The bits I found most interesting concerned Buffett’s concerns about corporate ethics. In general he sought to put his money behind individuals he felt he could trust, not only because he believed they could make money, but because of their ethics in business dealings. Of course, he didn’t always choose well. And sometimes he compromised—and later regretted certain choices. Continue reading “What Warren Buffett said about Ethics”
I recently had a series of conversations about how the public perceives two brands that I find intriguing: Bob’s Red Mill, a natural foods producer based in Oregon, and Hyperloop, a platform for rapid long distance transportation that is being implemented by a number of organizations all over the world. I took some notes—and created this post.
To me, the common thread between Bob’s Red Mill and Hyperloop is that they both let the people behind them represent them. It makes their value propositions credible in a way that clever writing and a huge creative budget can’t.
The recent news that Amazon inadvertently created gender-biased software for screening job applicants is a significant wake-up call for all organizations using AI. The software, which used machine learning to rank incoming resumes by comparison to resumes from people Amazon had already hired, could have discouraged recruiters from hiring women solely on the basis of their gender. Amazon, of all entities, should have known better. It should have expected and avoided this. If this can happen to Amazon, the question we really need to ask is: how many others are making the same mistake?
Summary:Every organization that processes data about any person in the EU must comply with the GDPR. Newly published GDPR Guidelines clarify that whenever an organization makes a decision using machine learning and personal data that has any kind of impact, a human must be able to independently review, explain, and possibly replace that decision using their own independent judgment. Organizations relying on machine learning models in the EU should immediately start planning how they are going to deliver a level of machine model interpretability sufficient for GDPR compliance. They should also examine how to identify whether any groups of people could be unfairly impacted by their machine models, and consider how to proactively avoid such impacts.
In October 2017, new Guidelines were published to clarify the EU’s GDPR (General Data Protection Regulation) with respect to “automated individual decision making.” These Guidelines apply to many machine learning models making decisions affecting EU citizens and member states. (A version of these Guidelines can be downloaded here—for reference, I provide page numbers from that document in this post.)
The purpose of this post is to call attention to how the GDPR, and these Guidelines in particular, may change how organizations choose to develop and deploy machine learning solutions that impact their customers.
Over the years I’ve discussed social media strategy with quite a few executives from large organizations. It’s no wonder so many approach social media with caution. They’re well aware of worst case scenarios, and as a byproduct, the majority of executives today still hesitate to play a highly visible personal role in social media, despite the best efforts of evangelists such as myself to drag them kicking and screaming into the 21st century.
Nonetheless, every major brand now recognizes the opportunity and necessity of engaging in social media conversations. And none that I know of are still relying exclusively on interns or just-out-of-school new hires to manage their programs. Projecting a brand presence into social media is a serious undertaking that requires communication skills, a certain amount of finesse, and common sense. This is where training and coaching come in, which is a topic for a future blog post, along with crisis preparation, which is the topic of a post I wrote that was published today in the Trapit blog.
Due to a misunderstanding, at the last minute before takeoff an airline refused to allow a pair of special-needs passengers to fly. This upset the passengers deeply and stranded them at an unfamiliar airport.
No one should have been surprised that intense criticism of the airline spread rapidly via social media, portraying them as bad-guys even though the incident was (arguably) a one-time mistake by an isolated group of employees.
This wound up being a good thing, because:
The airline discovered this issue, apologized to the would-be passengers and their families, refunded their money, offered them additional free flights, and came up with a new process to keep the problem from recurring. All-in-all, the airline—our hometown favorite here in Seattle, Alaska Airlines—took a regrettable mistake, and did everything possible (considering it was after the fact) to make it right with those affected. In this way Alaska Airlines also earned positive PR by showing they’re the kind of company that owns up to their mistakes and jumps on an opportunity to do the right thing when they can.
This post isn’t about Alaska Airlines—it’s about the other guys
I’m pleased to see more and more stories about companies turning customer complaints into positive publicity. But this post is for the other guys, anyone who isn’t sure they have the right attitude, either individually or organizationally, to handle all customer criticism in a positive way.
Junction is designed to help small and medium sized businesses plan, execute, and manage their social media initiatives effectively. Unlike many social media management solutions which offer publishing to social media accounts, monitoring conversations, and analytics as independent solutions, or as siloed components, Junction tightly integrates and dashboards all three.
I’ve noticed that a major stumbling block of many social media management solutions is that the feedback they offer about the success (or lack thereof) of social media efforts can be difficult to act on. Even when publishing, monitoring, and analytics are available under the same login, the gap between action and feedback can be wide enough to leave a major hurdle in the path of social media marketers. Meanwhile, the level of complexity conveyed by analytics tools can leave marketers, and the people they are accountable to, bewildered.