Getting fired from your job as an Amazon worker by an app

Clearly not a perspective born out of much empathy. Very few people have the requirements to even attempt to start a company and out of that limited pool, most fail within the first year, with luck playing a large role in which companies survive the first 3 years.

It's also bullshit - they do not want anyone to take that advice because employers cannot achieve anything without their employees so if even 20% of their workforce took that advice, their company would implode.

I can't think of any country in which the employee has to justify why they quit.

Allow employers to fire their employees without giving a cause for the first few months, giving them time to evaluate them. Problem solved.

One side of the sword makes hiring more difficult, the other side means you become homeless, lose your health insurance, and cannot provide for your family. It can literally be life threatening to the worker.

The power differential favours the employer so much in this situation that it is ridiculous.

I agree if there was a social safety net like UBI this would be a non issue, but we’re nowhere near that.

I’m not suggesting that there are no issues with algorithmic HR operations.

I’m suggesting that the abuses of such a system will only be solved in the courts, and that those courts decide the rules based on legislation that outlines worker protections.

Corporations exist to make a profit and you can’t expect them to act against the profit motive out of the kindness of their own hearts, you must compel them.

This is ultimately a human decision. The algorithm gives a score but a human decides what a good score is. Doing this at scale is no different than hiring a hundred middle managers to eye ball it.

At the end of the day- if your company wants to fire the least productive 10% of your workforce on a regular basis to motivate employees, you'll be able to do so.

This is a business practice issue and not a technology one

Are there people programming their "algorithms" with flags that say "if black, fire them"???

A lot of -isms are "hidden" -- for example, a manager might be saying to everyone that they do not discriminate, but in practice, they'll never choose people of certain race for promotions.

For human managers, this can be hard to prove, especially if they do not promote people very often. But for algorithms, it is much easier -- multiple people can inspect the source code for suspicious logic. And even if this is some sort of impenetrable AI model, you can still run it with different inputs, and check if the output changes if you change the input race.

I propose we extend the definition of algorism:

    al•go•rism /ˈælɡəɹɪzəm/
    n.
        1. Obsolete spelling of algorithm.
        2. An irrational belief in the infallibility of algorithms.

They almost certainly will not encode any explicit biases of the algorithm authors (e.g. “she’s pregnant, can we get rid of her?”), which is already a great step forward, as explicit bias is still sadly far too common in the workplace.

When implicit biases find their way into algorithms, it is usually a specific edge case that’s a function of the training data (e.g. facial recognition preforming poorly on darker skinned people due to training set imbalance). The biggest case of implicit bias in algorithms I’m aware of is when the training set is the behavior of society as a whole, as with search engines: for example, women search less for C-level jobs and so are less likely to be shown postings for C-level jobs when doing job searches (thereby perpetuating a vicious cycle), or websites depicting Black teens are more likely to show them in a criminal context than White teens, so searches for “black teenager” show pictures of criminals whereas searches for “white teenager” do not.

These implicit biases are not encoded by the algorithm developer but rather by the dataset the algorithm is applied to.

In the case of automated firing, I don’t see how implicit bias can creep in if the metrics are strictly work-related (e.g. fraction of on-time deliveries). For the record, I do not agree with automating performance metrics, since they cannot account for nuance (e.g. delivery drivers assigned to gated communities have issues opening the gate and thus deliver fewer on-time packages, which is not accounted for in the algorithm.) However, this is not a form of demographic bias, explicit or implicit.

>Imagine getting fired for something you have no control over, or didn't even realize was getting penalized for.

That is life, that could always happen, the right thing is to accept the fact and plan accordingly.

If i took any kind of job I acknowledge that there always possibility that I'm going to be fired for something I have no control.

Cruel, but legal.

Because the algorithm just bases its decisions on performance, without respect to context, you don't hear any meaningful accusations of bias.

When you give supervisors agency to make decisions you get accusations (real or imagined) about various biased actions, or get accusations that the non-binary pregnant Pacific Islanders are under-represented, or do not represent some other constituency. It's bullshit 80% of the time and generates alot of wasted time, cost and bad PR.

It's a policy decision made because it is better for the company and isn't unlike how supervisors in industrial settings operate. If you've ever seen a very physical factory workplace (where the employees are at-will), they tend to eject broken people with chronic injuries as they age out. When I worked at a mall food vendor in high school in the 90s, it was routine for employees to be terminated for being 5 minutes late or too slow. The bigger lesson is go to school and avoid working in some shitty job where your welfare is a lower priority than the machines you serve.

Not to mention the elephant in the room that an over-optimized environment is not suitable for human life.

I think the point of the OP is that these factors don't matter. Performance is the only goal. In general I think this is fine, but it's clear that the algorithm is not yet sound. Maybe we'll get there one day.

I kind of doubt Amazon's algorithm even uses machine learning, it probably assigns a score based on perf metrics (using a human-written algorithm), then fires employees below some cutoff.

Do you really need an AI to measure the performance(i.e require training as you said) ? I thibk a dumb counter would do the job.

>” trained on past data (which by definition includes all the "isms")”

Is that necessarily so?

Can’t they measure more objective data points like deliveries per hour, missdeliveries, mishandled/broken items (controlled for traffic, pop density, data from deliveries to similar areas, etc) rather than other things like “doesn’t get along with boss:peers, has bad breath, does cat-calls, etc?

Now, I admit this has too much Taylorism in it. To much of squeezing the last drop out of people, but I don’t think it has more soft bias.

Also, the patent taken by the author on the Manna system has expired.

I think the main discerning function is that most smart phone communication mechanisms are asynchronous first. For traditional dumb phones, the only async feature was really voice-mail and message recorder systems which, from my experience, lead right back to synchronous communication ("call me back please"). With the advent of SMS, we slowly pushed more into the world of async and people learned how to find more and more socially acceptable ways to increase those time delays to the point of ignoring. For myself, almost all of my comms on my phone are async. Synchronous communication is now the exception instead of the norm. If you call me, unless you're my family or very close friends, you will go to voice mail and you will be screened.

patience

hiring-and-firing robots are probably the next business innovation.

I mean it is what the bilionaires do and look where it got them. Seems our ancestors built a society that rewards it - and maybe we're in a place where it is unfixable due to the inertia of power.

So yeah.

If you're in the US, you very likely already live in a society that only values lives based on their output as given to someone else (unless you're in the capital holder class and happen to be on the other side of the table).

I think the tech industry is especially susceptible to arguments based on efficiency because after all, that's what technology provides at a fundamental basis. We need to realize, as with many aspects within technology itself, there are tradeoffs to efficiency itself. You still have educated people who will tout efficiency above all else. I'm all for progress and improving efficiency but it has costs (and benefits) on the human condition and we need to understand and factor those costs in instead of only looking at benefits from efficiency gains.

As a kid I was naive and believed this same philosophy of efficiency and didn't understand why we didn't always push to technology to improve situations. As I've grown over the years I realize life is a lot more than obsessing over efficiency. Obsessing over efficiency only brought me misery while accepting a reasonable degree of inefficiency in my life in strategic areas brought me a lot of happiness. It's always important to think about what you're optimizing on and what the costs are. Imagine going to a restaurant and making your dinner purchase decision based on a metric like: $/calorie. It's probably the most economically efficient way to look at food (perhaps with a nutritional distribution factor as well), but who wants to live like that out of choice?

But what if your program's performance is to 'determine when a worker has outlived their usefulness and needs to be let go?' (FTA)