Tag: bias

  • Eliminating Unconscious Bias in Tech Recruitment

    Eliminating Unconscious Bias in Tech Recruitment

    For a more diverse workforce, businesses need to be aware of the role of unconscious bias in recruitment and take the necessary steps to remove it where possible

    The business case for diverse organizations is clear: A more diverse and inclusive business consistently delivers better results. From a legal perspective, with a variety of discrimination legislation long since passed, there should be no barriers to hiring a workforce that is more reflective of society as a whole.

    So why, then, do we continually see data and hear of experiences where that is consistently not the case, across all sectors? What is holding back the acceleration of diversity within organizations?

    One problem is the role of unconscious bias in recruitment. We all have biases, formed from our individual reactions to a complex mix of cultural, social and economic experiences. The challenge is how these biases inform decision-making, both in our personal and professional lives. They have a direct impact on our actions and, from a business perspective, have significant consequences.

    In technology, this can result in our biases being programmed into solutions that are designed to help but end up hindering different parts of society. There have been numerous examples in which human biases have been, whether consciously or unconsciously, programmed into artificial intelligence tools, with those tools then making decisions that disproportionately affect certain sections of society.

    This happens when an organization doesn’t have a diverse enough workforce, one that is empowered to challenge decisions and highlight when products might have bias or preference built into them.

    This is why getting recruitment right is so important, yet even there, as highlighted before, unconscious bias has an effect. It means that even when presented with two candidates with exactly the same skills, experiences and qualifications, hiring managers are more likely to choose the person that aligns positively with their biases. This affects all professions—a Yale University study found that “both men and women science faculty were more likely to hire the male, ranked him higher in competency, and were willing to pay him $4,000 more than the woman. They were also more willing to provide mentoring to the male than to the female candidate.”

    How, therefore, do we root out unconscious bias within recruitment and ensure that it does not hinder the drive to create more inclusive technology workforces?

    First, it is important to realize that where you hire from is as important as how you hire. If you only ever recruit from the same schools or with the same websites, then you are leaving yourself at the mercy of the diversity of those institutions. If you only ever use one route to apply, whether that’s only using recruitment consultants or use a website that is only really accessible through a computer, then again you are limiting yourself to candidates with access to those pathways.

    Outside of reconsidering talent pools, one way to reduce unconscious bias is through the removal of identifying characteristics—no photos, no names, dates of birth or school and university grades. This can help to an extent, in that decision-makers have to go off the candidates’ experience and how they’ve presented their previous roles, but it does have its restrictions—with entry-level positions, for example, where relevant experience might be limited.

    That’s a quick-fix solution. For a more sustainable, thorough and long-term approach, recruiters—particularly those hiring for technology positions—need to look at how they can accurately judge skill sets. This can be challenging in itself—in larger organizations, those involved in early stage selection may not have the requisite background understanding to make the right choices.

    That is why having an anonymous, objective environment in which to appraise skills could be the answer. Using coding tests that closely resemble what the successful applicant will be doing will enable organizations to evaluate candidates and provide a clear appraisal of whether they would be suited for the position. This can then be taken and used alongside other tests that look at aptitude and cultural attributes to score the candidate on suitability.

    It is this combination of anonymity and focus on skills that help remove unconscious bias; as one study found, if a woman contributing to an open source software community was unidentifiable, their contribution was likely to be accepted more often than men’s. When a woman’s gender was identifiable, they were rejected more often. The message is clear and applicable to technology recruitment: Take the focus away from who is doing the work, and make it about the work, and you will see a more diverse workforce emerge (assuming the applicant pool is diverse to begin with).

    We need to have diverse employee bases; it is the only way that we can be sure that the tools and technologies being developed will serve to help society rather than to increase inequalities. To do that, businesses need to be aware of the role of unconscious bias in recruitment and take the necessary steps to remove it where possible. That means anonymity—but more importantly, it means focusing on skills and the ability to do the job in question.

  • Measuring What Matters in Evaluating Software Performance

    Measuring What Matters in Evaluating Software Performance

     When developing and deploying a new feature or application, performance (speed, reliability) must be closely analyzed. After all, there’s no benefit to delivering a product if it doesn’t work well or users are forced to stare at a spinning wheel. To get a true view of performance, many organizations rely on metrics—a smart move since metrics, much like the scale, do not lie. Right?

    Unfortunately, that’s not always the case. Even when metrics are used, there often are different interpretations of performance, as well as debate over which metrics should be used in the first place. Metrics can be biased and manipulated, but does that mean we should stop using them? Of course not. Instead, we must learn to weed out bias and metric manipulation.

    What is Bias?

    Bias is not a bad thing; everybody is biased to some degree. Bias is defined as a particular tendency, inclination, feeling or opinion, especially one that is preconceived.

    Two common biases are anchoring and confirmation bias. Anchoring is the tendency to rely on a single piece of information, usually the first piece of information we encounter. The statistics presented first will most likely be remembered and drive decisions, responses and reactions.

    Imagine you’re about to launch a new application, but before doing so you run some performance tests to see how fast the application loads. The initial tests show the load time is 10 seconds. That’s too high, so the team looks for ways to improve performance. A few weeks later response times drop to 6 seconds, a 40 percent improvement. That’s great, and everybody celebrates. But what if initial tests showed page load times of 4 seconds and a few months after launch times crept up 40 percent to 5.6 seconds. Would you still be celebrating? No, you would be looking at what went wrong and trying to fix it. The initial anchor influences whether you see a time as positive or negative. 5.6 seconds is better than 6 but the initial anchor makes 5.6 seem worse.

    Confirmation bias is the tendency to interpret and search for information that confirms a pre-existing hypothesis. If you believe a new feature does not add value to an application, you may be more likely to focus on negative statistics rather than positive ones. If testing shows application load times will increase 15 percent with the new feature, you may use that as proof to delay or stop the release of the feature. If, however, you think the feature is a real game-changer, you will find the research about the 20 percent rule, which says people perceive changes in speed when the difference is at least 20 percent faster or slower. A change in 15 percent won’t be perceived by users. At the core, confirmation bias is about ego and not wanting to be wrong.

    The first step to avoiding biases is acknowledging they exist and everybody—yes even you—is susceptible. But there are ways to avoid them, or at least minimize their impact. Create your own anchor. Do research to determine what is an acceptable response time and use that as the anchor point. Search for facts that support and oppose your viewpoint, and don’t stop with a single site or fact; find multiple sources. Foster an environment where disagreement is encouraged in a positive and constructive manner, and listen to the dissenting viewpoints. Ask better questions and be willing to accept the answers even if they aren’t what you were seeking.

    Avoiding Metric Manipulation

    Metrics should be actionable and tied to business goals as opposed to being purely “vanity metrics”—those metrics that may make everybody feel good, but don’t reflect the true user experience and the consequent impact on the business.

    Specific to measuring web performance, there are countless metrics that can be selected to determine whether a user experience is positive or negative. If you choose to measure the time to the onload event as the core metric, you can recode a page to defer items to after the onload event. The metric will go down, teams will celebrate, but did this have any impact on the user experience? Was the goal really to reduce onload time or was it to better satisfy end users through a faster site in their eyes, increasing conversions and time spent on site?

    Here is a quick checklist you can use to choose the right user performance metrics:

    • Do improvements or declines in this metric impact what really matters: the user perception and business performance? Can this correlation be clearly made? How will this metric show progress towards the businesses goals and objectives?
    • Can the metric be gamed or influenced? Once something is being measured, people may look for ways to game the system and show improvement when in actuality there is none.
    • Is the metric “good enough”—i.e., the best one available, although it may not be perfect? Though not a performance-related example, popular website Medium’s choice of top-line metric illustrates this approach. While most websites rely on unique site visitor numbers as the ultimate gauge for their success, Medium, which views high user engagement as their key success factor, instead measures the time users spend reading articles on the site. This may not reflect the totality of user engagement (visitors who come to the site, but may just scan, not read) but it’s good enough and a more realistic indicator than alternatives.

    Whether we like it or not, people are heavily guided by bias and emotion, and they often manipulate reality (even subconsciously) to support their own motivations. Performance metrics are and always will be central to evaluating software performance, but it’s not enough to just lay out the facts. Organizations must frame and present metrics appropriately and sufficiently, and focus on those that are the most relevant and reliable. These are the keys to delivering the most complete, impartial canvas of performance information, helping guide the best decisions in the interest of the business.

    About the Author / Dawn Parzych

    Dawn Parzych is Director of Product and Solution Marketing at Catchpoint. She enjoys researching, writing and speaking about trends related to application performance, user perception, and how they impact the digital experience. In 15+ year career, Dawn has held a wide variety of roles in the application performance space at Instart Logic, F5 Networks and Gomez. Connect with her on LinkedIn.