{"id":1941,"date":"2021-11-02T22:49:09","date_gmt":"2021-11-03T04:49:09","guid":{"rendered":"https:\/\/singularityumexicosummit.com\/?p=1941"},"modified":"2021-11-02T22:49:09","modified_gmt":"2021-11-03T04:49:09","slug":"algorithms-workers-cant-see-are-increasingly-pulling-the-management-strings","status":"publish","type":"post","link":"https:\/\/singularityumexico.com\/en\/algorithms-workers-cant-see-are-increasingly-pulling-the-management-strings\/","title":{"rendered":"Algorithms Workers Can\u2019t See Are Increasingly Pulling the Management Strings"},"content":{"rendered":"

\u201cI\u2019m sorry, Dave. I\u2019m afraid I can\u2019t do that.\u201d HAL\u2019s cold, if polite, refusal to open the pod bay doors in 2001: A Space Odyssey<\/a> <\/em>has become a defining warning about putting too much trust in artificial intelligence, particularly if you work in space.<\/p>\n\n\n\n

In the movies, when a machine decides to be the boss (or humans let it) things go wrong. Yet despite myriad dystopian warnings, control by machines is fast becoming our reality.<\/p>\n\n\n\n

Algorithms\u2014sets of instructions to solve a problem or complete a task\u2014now drive everything from browser search results to better medical care<\/a>.<\/p>\n\n\n\n

They are helping design buildings<\/a>. They are speeding up trading<\/a> on financial markets, making and losing fortunes in micro-seconds. They are calculating the most efficient routes for delivery drivers<\/a>.<\/p>\n\n\n\n

In the workplace, self-learning algorithmic computer systems are being introduced by companies to assist in areas such as hiring, setting tasks, measuring productivity, evaluating performance, and even terminating employment: \u201cI\u2019m sorry, Dave. I\u2019m afraid you are being made redundant.\u201d<\/p>\n\n\n\n

Giving self\u2010learning algorithms the responsibility to make and execute decisions affecting workers is called \u201calgorithmic management.<\/a>\u201d It carries a host of risks in depersonalizing management systems and entrenching pre-existing biases.<\/p>\n\n\n\n

At an even deeper level, perhaps, algorithmic management entrenches a power imbalance between management and worker. Algorithms are closely guarded secrets. Their decision-making processes are hidden. It\u2019s a black-box: perhaps you have some understanding of the data that went in, and you see the result that comes out, but you have no idea of what goes on in between.<\/p>\n\n\n\n

Algorithms at Work<\/h3>\n\n\n\n

Here are a few examples of algorithms already at work.<\/p>\n\n\n\n

At Amazon\u2019s fulfillment center in south-east Melbourne, they set the pace for \u201cpickers,\u201d who have timers on their scanners showing how long they have to find the next item<\/a>. As soon as they scan that item, the timer resets for the next. All at a \u201cnot quite walking, not quite running\u201d speed.<\/p>\n\n\n\n

Or how about AI determining your success in a job interview? More than 700 companies have trialed such technology<\/a>. US developer HireVue says its software speeds up the hiring process by 90 percent by having applicants answer identical questions and then scoring them according to language, tone, and facial expressions.<\/p>\n\n\n\n

Granted, human assessments during job interviews are notoriously flawed. Algorithms,however, can also be biased<\/a>. The classic example is the COMPAS software used by US judges, probation, and parole officers to rate a person\u2019s risk of re-offending. In 2016 a ProPublica investigation<\/a> showed the algorithm was heavily discriminatory, incorrectly classifying black subjects as higher risk 45 percent of the time, compared with 23 percent for white subjects.<\/p>\n\n\n\n

How Gig Workers Cope<\/h3>\n\n\n\n

Algorithms do what their code tells them to do. The problem is this code is rarely available. This makes them difficult to scrutinize, or even understand.<\/p>\n\n\n\n

Nowhere is this more evident than in the gig economy. Uber, Lyft, Deliveroo, and other platforms could not exist without algorithms allocating, monitoring, evaluating, and rewarding<\/a> work.<\/p>\n\n\n\n

Over the past year Uber Eats\u2019 bicycle couriers<\/a> and drivers<\/a>, for instance, have blamed unexplained changes to the algorithm for slashing their jobs<\/a>, and incomes.<\/p>\n\n\n\n

Rider\u2019s can\u2019t be 100 percent sure it was all down to the algorithm. But that\u2019s part of the problem. The fact those who depend on the algorithm don\u2019t know one way or the other has a powerful influence on them.<\/p>\n\n\n\n

This is a key result from our interviews with 58 food-delivery couriers<\/a>. Most knew their jobs were allocated by an algorithm (via an app). They knew the app collected data. What they didn\u2019t know was how data was used to award them work.<\/p>\n\n\n\n

In response, they developed a range of strategies (or guessed how) to \u201cwin\u201d more jobs, such as accepting gigs as quickly as possible and waiting in \u201cmagic\u201d locations. Ironically, these attempts to please the algorithm<\/a> often meant losing the very flexibility that was one of the attractions of gig work.<\/p>\n\n\n\n

The information asymmetry created by algorithmic management has two profound effects. First, it threatens to entrench systemic biases, the type of discrimination hidden within the COMPAS algorithm for years. Second, it compounds the power imbalance<\/a> between management and worker.<\/p>\n\n\n\n

Our data also confirmed others\u2019 findings that it is almost impossible to complain about the decisions of the algorithm. Workers often do not know the exact basis of those decisions, and there\u2019s no one to complain to anyway. When Uber Eats bicycle couriers asked for reasons about their plummeting income, for example, responses from the company advised them \u201cwe have no manual control<\/a> over how many deliveries you receive.\u201d<\/p>\n\n\n\n

Broader Lessons<\/h3>\n\n\n\n

When algorithmic management operates as a \u201cblack box\u201d one of the consequences is that it is can become an indirect control mechanism<\/a>. Thus far under-appreciated by Australian regulators, this control mechanism has enabled platforms to mobilize a reliable and scalable workforce while avoiding employer responsibilities<\/a>.<\/p>\n\n\n\n

\u201cThe absence of concrete evidence about how the algorithms operate\u201d, the Victorian government\u2019s inquiry into the \u201con-demand\u201d workforce<\/a> notes in its report, \u201cmakes it hard for a driver or rider to complain if they feel disadvantaged by one.\u201d<\/p>\n\n\n\n

The report, published in June, also found it is \u201chard to confirm if concern over algorithm transparency is real.\u201d<\/p>\n\n\n\n

But it is precisely the fact it is hard to confirm that\u2019s the problem. How can we start to even identify, let alone resolve, issues like algorithmic management?<\/p>\n\n\n\n

Fair conduct standards to ensure transparency and accountability are a start. One example is the Fair Work initiative<\/a>, led by the Oxford Internet Institute<\/a>. The initiative is bringing together researchers with platforms, workers, unions, and regulators to develop global principles for work in the platform economy. This includes \u201cfair management,\u201d which focuses on how transparent the results and outcomes of algorithms are for workers.<\/p>\n\n\n\n

Understandings about impact of algorithms on all forms of work is still in its infancy. It demands greater scrutiny and research. Without human oversight based on agreed principles we risk inviting HAL into our workplaces.<\/p>\n\n\n\n

\n\n\n\n

This article is republished from\u00a0<\/em>The Conversation<\/a>\u00a0under a Creative Commons license. Read the\u00a0original article<\/a>.<\/em><\/p>\n\n\n\n

Image Credit:\u00a0PickPik<\/a><\/em><\/p>\n\n\n\n

Author:<\/strong><\/p>\n\n\n\n

I am a PhD in employment relations, with a specialty in the geography of work, that is, I argue that where work occurs is significant for understanding how, and on what terms, work occurs. Learn More<\/a><\/p>\n\n\n\n

Original Article<\/a><\/p>","protected":false},"excerpt":{"rendered":"

\u201cI\u2019m sorry, Dave. I\u2019m afraid I can\u2019t do that.\u201d HAL\u2019s cold, if polite, refusal to open the pod bay doors in 2001: A Space Odyssey has become a defining warning about putting too much trust in artificial intelligence, particularly if you work in space. In the movies, when a machine decides to be the boss (or humans […]\n","protected":false},"author":1,"featured_media":1942,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"episode_type":"","audio_file":"","podmotor_file_id":"","podmotor_episode_id":"","cover_image":"","cover_image_id":"","duration":"","filesize":"","filesize_raw":"","date_recorded":"","explicit":"","block":"","footnotes":""},"categories":[13],"tags":[26,43,44,27],"series":[],"class_list":["post-1941","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articulos-ingles","tag-artificial-intelligence","tag-future-of-work","tag-futuro-del-trabajo","tag-inteligencia-artificial-2"],"episode_featured_image":"https:\/\/singularityumexico.com\/wp-content\/uploads\/2021\/11\/algorithm-management-puppet-on-strings.jpg","episode_player_image":"https:\/\/singularityumexico.com\/wp-content\/uploads\/2023\/05\/11711533-1673157178559-89a95be153719-4-scaled.jpg","download_link":"","player_link":"","audio_player":false,"episode_data":{"playerMode":"dark","subscribeUrls":{"apple_podcasts":{"key":"apple_podcasts","url":"","label":"Apple Podcasts","class":"apple_podcasts","icon":"apple-podcasts.png"},"stitcher":{"key":"stitcher","url":"","label":"Stitcher","class":"stitcher","icon":"stitcher.png"},"google_podcasts":{"key":"google_podcasts","url":"","label":"Google Podcasts","class":"google_podcasts","icon":"google-podcasts.png"},"spotify":{"key":"spotify","url":"","label":"Spotify","class":"spotify","icon":"spotify.png"}},"rssFeedUrl":"https:\/\/singularityumexico.com\/en\/feed\/podcast\/the-feedback-loop-by-singularity","embedCode":"

Algorithms Workers Can\u2019t See Are Increasingly Pulling the Management Strings<\/a><\/blockquote>