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Cu privire la modul in care recunosc prejudecatile umane si cum sa ma descurc

Un cuvant despre modul in care noi, practicienii de date, ar trebui sa fim mai constienti si mai atenti la propriile prejudecati

(poveste postata initial pe Medium)

In lumea stiintei datelor, definim partinirea drept fenomenul in care un sistem suprageneralizeaza din datele sale si invata un lucru gresit. Cand se intampla acest lucru, prima actiune obisnuita pe care o luam este sa indreptam degetele catre datele sau procesul de formare, urmata de a spune „aceste date sunt rele” sau „Ar trebui sa-mi ajustez in continuare hiperparametrii”. Sigur, acest lucru ar putea fi o parte a problemei. escorte bucuresti total Cu toate acestea, inainte de a petrece mai mult timp si putere de procesare, as dori sa va invit sa va opriti, sa faceti un pas inapoi si sa ne ganditi la modul in care au ajuns datele pe care le folosim si, mai important, sa ne motivam despre modul in care interpretam aceasta.

Unlike machines and smart learners, we humans, suffer from bias, a bias that could have been introduced for numerous reasons, such as by moments we have previously experienced or concepts and definitions that are already part of who we are. Unfortunately, this bias could influence the way we handle, and interpret data, creating a problem when we, inadvertently, transfer those ideas and assumptions into our dataset, and consequently to our machine learning models and their outcome. escorte caras Examples of these consequences are often mentioned in the media (generally with headers that include an undesirable dose of fearmongering) such as the case of the famous ‘sexist’ recruitment model from Amazon that preferred male prospect candidates over female ones.

In acest articol, discut trei surse de partinire, prejudecata de confirmare, disponibilitatea euristica si partinirea probelor si scriu despre modul in care le-am recunoscut prezenta si efectul, alaturi de mai multe tehnici pe care le aplic pentru a le trata.

Din 2016 lucrez la echipa Antispam a unei platforme de intalniri si socializare, unde obiectivul meu este sa creez solutii pentru a detecta spammerii si a evita proliferarea acestora. escorte tulcea La inceputul carierei mele la companie, nu aveam in totalitate cunostinte despre utilizatorii nostri (asa cum era de asteptat); Nu am cunoscut pe deplin demografia noastra si nici modelul comportamental al acestora. Ce vreau sa spun este ca, dintr-o simpla privire, nu am putut spune daca un utilizator a fost spamer sau nu; oricine ar putea fi unul! Apoi, cu fiecare zi care trece, incepi sa experimentezi si sa inveti lucruri. Ah, aceasta regiune geografica pare a fi mai spammy, ah, acest domeniu de e-mail este o veste groaznica, ah, nume ca acestea nu sunt niciodata bune,si asa mai departe. escorte tranny bucuresti In cuvinte mai simple, am creat un profil mental al ceea ce un spammer se bazeaza pur si simplu pe ceea ce am invatat, am vazut si am tratat. Acum trebuie sa ma intreb: este aceasta cunoastere corecta? Este acest profil reprezentativ pentru intreaga populatie? Portretul meu mental al spammerului „ideal” este unul impartial? Acestea sunt cateva dintre intrebarile pe care mi le pun de fiecare data cand lucrez cu date si, cel mai important, de fiecare data cand antrenez un nou model de invatare a masinilor. De ce imi puneam aceste intrebari? Ei bine, pentru inceput, cred ca in aceasta linie de lucru ar trebui sa te intrebi mereu pe tine. escorte ciuc In al doilea rand, pentru ca asa recunosc partinirea bazata pe oameni si efectul pe care l-ar putea avea daca il ignor.

Dintre numeroasele prejudecati existente, exista trei principale – prejudecata de confirmare, partinire de disponibilitate si prejudecata de selectie – care cred ca ar putea provoca un efect nedorit in modelele mele daca nu le-as lua in considerare; asta nu inseamna ca nu ma deranjeaza celelalte prejudecati, ci doar ca acestea sunt cele care ma tin degetele de la picioare. In urmatoarele cateva randuri, voi defini aceste prejudecati si voi da cateva exemple despre cum m-ar putea obtine. escorte pitești

Confirmation bias, a type of cognitive bias, refers to the tendency of interpreting information, evidence, and data, in a way that supports and confirms a person’s views and hypotheses while disregarding any possible conflicting proof.



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The confirmation bias is one of the most common ones, and it is not hard to imagine why this is the case, after all, favoring and confirming ideas based on what we support sounds, in some way, like the logical thing to do. Earlier, I mentioned some possible theories and characteristics that I could learn about the spammers after working with them for such a long time, for example, the likelihood of a user being a spammer if it is located at a specific region. escorte ploieati This fact is plausible. There are some regions with a higher concentration of spammers than others, and because this pattern is somehow common, I might “unconsciously” learn and confirm that if user X comes from region Y, he might be a spammer. But is this enough reason to conclude that this user is an actual spammer? Of course not! Nonetheless, under specific and unfavorable circumstances, for example, if I had to flag a user profile during a stressful day, I could accidentally mark the user as a suspicious one, and thus confirming my biased belief just because my hypothesis stated that this user might indeed be a spammer. escorte covasna Nonetheless, I seldom do this manually, so the chances of this happening are almost 0.

The availability heuristic, another cognitive bias, describes the tendency of giving importance to the most recent and immediate experience, information, or example a person think of, whenever it encounters a decision-making situation. The main idea behind this mental shortcut is that if a person remembers a piece of information, it must mean that said information is essential. top escorte cluj When dealing with data, and decision systems, ignoring the existence of this assumption can lead to disastrous results. Here’s why.

Usually, during my working hours, colleagues approach me asking whether a profile is a spammy one or not. pret escorte bucuresti Usually, I answer right away because I am well familiar with how a spammer look like (do I sound biased?). Having said that, I must admit that there have been cases in which I am reluctant to answer with a quick yes or no without giving it a second thought. Why’s this? Because I’m sure that I have seen a case like that. escorte trapezului For example, daily I see many profiles and their usernames, and I have familiarized many patterns and keywords that indicates if said username relates to a spam profile. So, if you would randomly ask me, what’s a typical spammy username, I might have the answer.

Another example is while labeling data. escorte saveni Even though this process is an automatic one, every now and then I dive into the dataset looking for outliers or strange cases that require a human eye.



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During these expeditions through a sea of rows and features, I might see a particular example in which my brain, through the availability heuristic, might determine that a profile is a spammy or good one based on a recent experience. In such case, the easiest solution would be to listen to the little voice in my brain, and just switch the label (which honestly I’d do if I am a 100% sure), however, since I am aware of this bias, I’d first consult our other sources to confirm or deny my beliefs. publi24 sibiu escorte

Lastly, there is the sample bias, a statistical bias. This kind of bias is observed when the data selected for training your system does not represent the population in which the model will be used. The outcome is most likely a biased sample, a dataset that over-represents a group(s) and under-represent others. escorte sector 6 Getting rid of this bias is not an easy task and will most likely occur in practice because, as Wikipedia says, it is “practically impossible to ensure perfect randomness in sampling,” however, being aware of its existence could help to alleviate its influence. There are, probably infinite ways, in which this bias could show up in my day-to-day, and in the next paragraph, I illustrate some of the ones I have identified.

For starters, I am always thinking about timezones. escorte dublin That’s because every time I do something time related, for example, selecting X data from the last Y hours, my sample will be mostly made of observations from the geographical region that was at its peak time in those Y hours. For example, I am in Europe, so if at 9 am I do a query to select X thing from the last hour, my sample will most likely be made of European users and people with insomnia. So, in some way, I am adding bias to my sampled data. escorte publi24 cluj Another case I have identified is the differences between platforms and app versions. While querying data, we have to keep in mind that users are using different platforms, or release version of the app, meaning that they might be generating distinct kinds of data. For example, suppose that one day a product team decides that in the next version of the app, users will be allowed to upload a million images to their profiles. escorte anal brasov Then by any random and a very unfortunate chance, on that same day, I decided to build a model that detects spammers based on the number of pictures without being aware of such change in the app. Then, since the “million images” feature is new and not everybody will have the update, I won’t have a good representation of this new group of people who has a million images on their profiles, which will result in some unwanted results during training and inference time.

Solutions?

Is there a way to completely avoid human-based bias? I don’t know, but I am sure there are steps that we, as practitioners, can take to mitigate their effects in our dataset, and consequently, our decision-making models. escorte cluj

My first recommendation is to be data-driven. I don’t mean data-driven in the sense of “oh yes I read my data before making a decision” and making a few queries.



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What I mean is to squeeze, be one, delve, and heck, love those datasets. escorte amalia sibiu Draw their distributions, remove outliers, clusters them, test them, reduce their dimensionality and so on. Make sure you truly know them.

Another tip that goes hand to hand with the previous one is to identify the possible sources of bias. escorte brasov Write on a sheet of paper, wiki page, sticky note, or on the back of your hand, what could introduce bias to your system. Is it time, or differences across the app versions, like I mentioned? Question yourself the same way I did. Ask yourself if your sample data is representative of the population, or if the decision you are about to make is based on a genuine piece of information or on a gut feeling you have because of that data point you remember from yesterday. escorte bucuresi

Lastly, share your process with others. Talk to the person next to you, and ask them what they think about your code or query, or create a pull request so that others can scrutiny your work. Sometimes because we are so close and attached to the material, we fail to see mistakes and details that others could detect. escorte bucuesti

Conclusion

We, humans, are biased. If this human-based bias is not handled correctly, it might affect the way we work and interpret data, and ultimately it will influence the outcome — which in most cases is a non-desirable one — and performance of our machine learning models. In this article, I introduced three kinds of biases: confirmation bias, availability heuristic, and sample bias, and talked about the many ways they could manifest in my daily work and offered some suggestions on how we could lighten their impact. escorte cluh

Ignoring the existence of these biases could cause unwanted and disastrous behavior in our system, responses that in the majority of the cases would leave us with the “heh? Why does my model believe this is a monkey?”-kind-of-questions, resulting in sensationalist and fearmongering articles stating that AI is racist, sexist, elitist or just plainly unjust. I sincerely believe that every person who works with data should be aware of the impact this could have on their work. With the rapid adoption of machine learning in every facet of our life, our products could turn out to be a biased system that unfortunately could be responsible for causing a fatal accident, diagnosing an incorrect treatment or blocking your whole userbase.

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Video Description:

Un cuvant despre modul in care noi, practicienii de date, ar trebui sa fim mai constienti si mai atenti la propriile prejudecati(poveste postata initial pe Medium)In lumea stiintei datelor, definim partinirea drept fenomenul in care un sistem suprageneralizeaza din datele sale si invata un lucru gresit. Cand se intampla acest lucru, prima actiune obisnuita pe care o luam este sa indreptam degetele catre datele sau procesul de formare, urmata de a spune „aceste date sunt rele” sau „Ar trebui sa-mi ajustez in continuare hiperparametrii”. Sigur, acest lucru ar putea fi o parte a problemei. escorte bucuresti total Cu toate acestea, inainte de a petrece mai mult timp si putere de procesare, as dori sa va invit sa va opriti, sa faceti un pas inapoi si sa ne ganditi la modul in care au ajuns datele pe care le folosim si, mai important, sa ne motivam despre modul in care interpretam aceasta.Unlike machines and smart learners, we humans, suffer from bias, a bias that could have been introduced for numerous reasons, such as by moments we have previously experienced or concepts and definitions that are already part of who we are. Unfortunately, this bias could influence the way we handle, and interpret data, creating a problem when we, inadvertently, transfer those ideas and assumptions into our dataset, and consequently to our machine learning models and their outcome. escorte caras Examples of these consequences are often mentioned in the media (generally with headers that include an undesirable dose of fearmongering) such as the case of the famous ‘sexist’ recruitment model from Amazon that preferred male prospect candidates over female ones.In acest articol, discut trei surse de partinire, prejudecata de confirmare, disponibilitatea euristica si partinirea probelor si scriu despre modul in care le-am recunoscut prezenta si efectul, alaturi de mai multe tehnici pe care le aplic pentru a le trata.Din 2016 lucrez la echipa Antispam a unei platforme de intalniri si socializare, unde obiectivul meu este sa creez solutii pentru a detecta spammerii si a evita proliferarea acestora. escorte tulcea La inceputul carierei mele la companie, nu aveam in totalitate cunostinte despre utilizatorii nostri (asa cum era de asteptat); Nu am cunoscut pe deplin demografia noastra si nici modelul comportamental al acestora. Ce vreau sa spun este ca, dintr-o simpla privire, nu am putut spune daca un utilizator a fost spamer sau nu; oricine ar putea fi unul! Apoi, cu fiecare zi care trece, incepi sa experimentezi si sa inveti lucruri. Ah, aceasta regiune geografica pare a fi mai spammy, ah, acest domeniu de e-mail este o veste groaznica, ah, nume ca acestea nu sunt niciodata bune,si asa mai departe. escorte tranny bucuresti In cuvinte mai simple, am creat un profil mental al ceea ce un spammer se bazeaza pur si simplu pe ceea ce am invatat, am vazut si am tratat. Acum trebuie sa ma intreb: este aceasta cunoastere corecta? Este acest profil reprezentativ pentru intreaga populatie? Portretul meu mental al spammerului „ideal” este unul impartial? Acestea sunt cateva dintre intrebarile pe care mi le pun de fiecare data cand lucrez cu date si, cel mai important, de fiecare data cand antrenez un nou model de invatare a masinilor. De ce imi puneam aceste intrebari? Ei bine, pentru inceput, cred ca in aceasta linie de lucru ar trebui sa te intrebi mereu pe tine. escorte ciuc In al doilea rand, pentru ca asa recunosc partinirea bazata pe oameni si efectul pe care l-ar putea avea daca il ignor.Dintre numeroasele prejudecati existente, exista trei principale - prejudecata de confirmare, partinire de disponibilitate si prejudecata de selectie - care cred ca ar putea provoca un efect nedorit in modelele mele daca nu le-as lua in considerare; asta nu inseamna ca nu ma deranjeaza celelalte prejudecati, ci doar ca acestea sunt cele care ma tin degetele de la picioare. In urmatoarele cateva randuri, voi defini aceste prejudecati si voi da cateva exemple despre cum m-ar putea obtine. escorte pitești Confirmation bias, a type of cognitive bias, refers to the tendency of interpreting information, evidence, and data, in a way that supports and confirms a person’s views and hypotheses while disregarding any possible conflicting proof. escorte timidoaraescorte resitaescorte publi24escorte baicoiescorte bucursstiescorte ploiesti ieftineescorte calarasiescorte ploiestescorte hotel bucurestiescorte satu mareescorte rădăuțiescorte militari residenceescorte din clujescorte sibescorte baia mareescorte albamiercurea ciuc escorteescorte roscateescorte lux oradeaescorte alexandria The confirmation bias is one of the most common ones, and it is not hard to imagine why this is the case, after all, favoring and confirming ideas based on what we support sounds, in some way, like the logical thing to do. Earlier, I mentioned some possible theories and characteristics that I could learn about the spammers after working with them for such a long time, for example, the likelihood of a user being a spammer if it is located at a specific region. escorte ploieati This fact is plausible. There are some regions with a higher concentration of spammers than others, and because this pattern is somehow common, I might “unconsciously” learn and confirm that if user X comes from region Y, he might be a spammer. But is this enough reason to conclude that this user is an actual spammer? Of course not! Nonetheless, under specific and unfavorable circumstances, for example, if I had to flag a user profile during a stressful day, I could accidentally mark the user as a suspicious one, and thus confirming my biased belief just because my hypothesis stated that this user might indeed be a spammer. escorte covasna Nonetheless, I seldom do this manually, so the chances of this happening are almost 0.The availability heuristic, another cognitive bias, describes the tendency of giving importance to the most recent and immediate experience, information, or example a person think of, whenever it encounters a decision-making situation. The main idea behind this mental shortcut is that if a person remembers a piece of information, it must mean that said information is essential. top escorte cluj When dealing with data, and decision systems, ignoring the existence of this assumption can lead to disastrous results. Here’s why.Usually, during my working hours, colleagues approach me asking whether a profile is a spammy one or not. pret escorte bucuresti Usually, I answer right away because I am well familiar with how a spammer look like (do I sound biased?). Having said that, I must admit that there have been cases in which I am reluctant to answer with a quick yes or no without giving it a second thought. Why’s this? Because I’m sure that I have seen a case like that. escorte trapezului For example, daily I see many profiles and their usernames, and I have familiarized many patterns and keywords that indicates if said username relates to a spam profile. So, if you would randomly ask me, what’s a typical spammy username, I might have the answer.Another example is while labeling data. escorte saveni Even though this process is an automatic one, every now and then I dive into the dataset looking for outliers or strange cases that require a human eye. escorte cisnadieescorte sala palatuluiescorte oradeaescorte mioveniescorte dorohoiescorte zimniceaescorte transexualiescorte sexi baia mareescorte testateescorte bucur oborescorte brpoze escorteescorte mmescorte transexuale bucurestiescorte bucescorte devaescorte lux romaniaescorte titanescorte vranceaescorte nicolae grigorescu During these expeditions through a sea of rows and features, I might see a particular example in which my brain, through the availability heuristic, might determine that a profile is a spammy or good one based on a recent experience. In such case, the easiest solution would be to listen to the little voice in my brain, and just switch the label (which honestly I’d do if I am a 100% sure), however, since I am aware of this bias, I’d first consult our other sources to confirm or deny my beliefs. publi24 sibiu escorte Lastly, there is the sample bias, a statistical bias. This kind of bias is observed when the data selected for training your system does not represent the population in which the model will be used. The outcome is most likely a biased sample, a dataset that over-represents a group(s) and under-represent others. escorte sector 6 Getting rid of this bias is not an easy task and will most likely occur in practice because, as Wikipedia says, it is “practically impossible to ensure perfect randomness in sampling,” however, being aware of its existence could help to alleviate its influence. There are, probably infinite ways, in which this bias could show up in my day-to-day, and in the next paragraph, I illustrate some of the ones I have identified.For starters, I am always thinking about timezones. escorte dublin That’s because every time I do something time related, for example, selecting X data from the last Y hours, my sample will be mostly made of observations from the geographical region that was at its peak time in those Y hours. For example, I am in Europe, so if at 9 am I do a query to select X thing from the last hour, my sample will most likely be made of European users and people with insomnia. So, in some way, I am adding bias to my sampled data. escorte publi24 cluj Another case I have identified is the differences between platforms and app versions. While querying data, we have to keep in mind that users are using different platforms, or release version of the app, meaning that they might be generating distinct kinds of data. For example, suppose that one day a product team decides that in the next version of the app, users will be allowed to upload a million images to their profiles. escorte anal brasov Then by any random and a very unfortunate chance, on that same day, I decided to build a model that detects spammers based on the number of pictures without being aware of such change in the app. Then, since the “million images” feature is new and not everybody will have the update, I won’t have a good representation of this new group of people who has a million images on their profiles, which will result in some unwanted results during training and inference time.Solutions?Is there a way to completely avoid human-based bias? I don’t know, but I am sure there are steps that we, as practitioners, can take to mitigate their effects in our dataset, and consequently, our decision-making models. escorte cluj My first recommendation is to be data-driven. I don’t mean data-driven in the sense of “oh yes I read my data before making a decision” and making a few queries. escorteescorte bbwescorte alba iuliaescorte publiescorte de companieescorte timisoarescorte slatinaforum escorteescorte magherupubli24 escorte timisoaraescorte galati deplasariescorte avrigescorte recomandateforum escorte brailastudente escorteescorte ploiestimihaela escorteescorte...iasiescorte bihorpublic escorte What I mean is to squeeze, be one, delve, and heck, love those datasets. escorte amalia sibiu Draw their distributions, remove outliers, clusters them, test them, reduce their dimensionality and so on. Make sure you truly know them.Another tip that goes hand to hand with the previous one is to identify the possible sources of bias. escorte brasov Write on a sheet of paper, wiki page, sticky note, or on the back of your hand, what could introduce bias to your system. Is it time, or differences across the app versions, like I mentioned? Question yourself the same way I did. Ask yourself if your sample data is representative of the population, or if the decision you are about to make is based on a genuine piece of information or on a gut feeling you have because of that data point you remember from yesterday. escorte bucuresi Lastly, share your process with others. Talk to the person next to you, and ask them what they think about your code or query, or create a pull request so that others can scrutiny your work. Sometimes because we are so close and attached to the material, we fail to see mistakes and details that others could detect. escorte bucuesti ConclusionWe, humans, are biased. If this human-based bias is not handled correctly, it might affect the way we work and interpret data, and ultimately it will influence the outcome — which in most cases is a non-desirable one — and performance of our machine learning models. In this article, I introduced three kinds of biases: confirmation bias, availability heuristic, and sample bias, and talked about the many ways they could manifest in my daily work and offered some suggestions on how we could lighten their impact. escorte cluh Ignoring the existence of these biases could cause unwanted and disastrous behavior in our system, responses that in the majority of the cases would leave us with the “heh? Why does my model believe this is a monkey?”-kind-of-questions, resulting in sensationalist and fearmongering articles stating that AI is racist, sexist, elitist or just plainly unjust. I sincerely believe that every person who works with data should be aware of the impact this could have on their work. With the rapid adoption of machine learning in every facet of our life, our products could turn out to be a biased system that unfortunately could be responsible for causing a fatal accident, diagnosing an incorrect treatment or blocking your whole userbase. Subscribe to Juan De Dios Santos Get the latest posts delivered right to your inbox Great! Check your inbox and click the link to confirm your subscription. Please enter a valid email address!

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