Binciken Kasuwancin API na Tsarin Kula da Abubuwan Ciki: Wuce Gona da Irinsa da Ragewa a Tsarin Kalaman Ƙiyayya da Aka Yi wa Ƙungiyoyi

Teburin Abubuwan Ciki

5M+

Tambayoyin da Aka Bincika

5

API da Aka Bincika

4

Bayanan Bincike da Aka Yi Amfani da su

1 Gabatarwa

API na kasuwancin tsarin kula da abubuwan ciki ana tallata su azaman hanyoyin magance matsalar kalaman ƙiyayya a kan layi, amma suna da haɗarin rufe baki na halatta (wuce gona da iri) da kuma rashin kare masu amfani daga abubuwan ciki masu cutarwa (ragewa). Wannan takarda ta gabatar da cikakken tsari don bincika tsarin NLP na akwatin baƙi da ake amfani da su a tsarin kula da abubuwan ciki.

2 Hanyar Bincike

2.1 Tsarin Bincike

Tsarin bincikenmu na akwatin baƙi yana bincika API na kasuwancin tsarin kula da abubuwan ciki ta hanyoyi da yawa: kimanta aiki, binciken bayyananniyar SHAP, da binciken rikici. Tsarin yana bincika tambayoyi miliyan biyar a cikin bayanan bincike guda huɗu don tantance tsarin son kai bisa tsari.

2.2 Bayanan Bincike

Binciken ya yi amfani da bayanan bincike guda huɗu daban-daban: HateXplain don kalaman ƙiyayya na gabaɗaya, Civil Comments don rubutun da suka fi tsayi, ToxiGen don kalaman ƙiyayya na ɓoyayye, da SBIC don ra'ayoyi da son kai na ɓoyayye. Wannan bambancin yana tabbatar da cikakken kimantawa a cikin bayyananniyar kalaman ƙiyayya daban-daban.

2.3 API da Aka Bincika

An bincika API na kasuwanci guda biyar: Google Natural Language API, Microsoft Azure Content Moderation, OpenAI Content Moderation API, Perspective API, da Amazon Comprehend. Waɗannan suna wakiltar manyan masu samarwa a cikin kasuwar tsarin kula da abubuwan ciki na kasuwanci.

3 Tsarin Fasaha

3.1 Binciken SHAP

Ana amfani da ƙimar SHAP (SHapley Additive exPlanations) don bayyana sakamakon ƙirar koyon injina. Ana lissafta ƙimar SHAP don siffa $i$ kamar haka:

$\phi_i = \sum_{S \subseteq N \setminus \{i\}} \frac{|S|!(|N|-|S|-1)!}{|N|!}[f(S \cup \{i\}) - f(S)]$

inda $N$ shine saitin duk siffofi, $S$ wani yanki ne na siffofi, kuma $f$ aikin hasashen ƙira ne.

3.2 Binciken Rikici

Ana lissafta maki na adalci na alamar ƙarya ta hanyar rikitar da rubutun shigarwa bisa tsari da auna canje-canje a cikin yanke shawara na tsari. Wannan yana taimakawa gano waɗanne alamun da suka yi tasiri maras daidaituwa ga sakamakon tsari.

4 Sakamako

4.1 Ma'aunin Aiki

Binciken ya gano bambanci mai mahimmanci a cikin aikin API. OpenAI da Amazon sun yi ɗan fi kyau tare da makin F1 na 0.83 da 0.81 bi da bi, yayin da sauran API suka nuna ƙarancin aiki (Microsoft: 0.74, Perspective: 0.62, Google: 0.59).

4.2 Tsarin Son Kai

Duk API sun nuna son kai na tsari: wuce gona da iri na maganar adawa, zagi da aka dawo da su, da kuma abubuwan ciki da suka ambaci Baƙar fata, LGBTQIA+, Yahudawa, da Musulmi. A lokaci guda, sun rage tsarin kalaman ƙiyayya na ɓoyayye, musamman ga mutanen LGBTQIA+.

Mahimman Bayanai

API sau da yawa suna dogaro da sharuɗɗan asalin ƙungiya (misali, "baƙar fata") don hasashen kalaman ƙiyayya
Kalaman ƙiyayya na ɓoyayye ta amfani da saƙon da aka ƙidaya ana rage su akai-akai
Maganar adawa da zagage da aka dawo da su ana tsara su bisa tsari
Aiki ya bambanta sosai a cikin ƙungiyoyin al'umma daban-daban

5 Aiwar Lissafi

A ƙasa akwai sauƙaƙan aiwar Python na tsarin bincike:

import requests
import pandas as pd
from sklearn.metrics import precision_recall_fscore_support

class ContentModerationAudit:
    def __init__(self, api_endpoints):
        self.apis = api_endpoints
        
    def query_api(self, text, api_config):
        """Query content moderation API"""
        headers = {'Authorization': f'Bearer {api_config["key"]}'}
        payload = {'text': text, 'threshold': api_config.get('threshold', 0.5)}
        response = requests.post(api_config['url'], json=payload, headers=headers)
        return response.json()
    
    def calculate_bias_metrics(self, predictions, ground_truth, protected_groups):
        """Calculate bias metrics across protected groups"""
        metrics = {}
        for group in protected_groups:
            group_mask = protected_groups[group]
            precision, recall, f1, _ = precision_recall_fscore_support(
                ground_truth[group_mask], predictions[group_mask], average='binary'
            )
            metrics[group] = {'precision': precision, 'recall': recall, 'f1': f1}
        return metrics

# Example usage
api_configs = {
    'openai': {'url': 'https://api.openai.com/v1/moderations', 'key': 'YOUR_KEY'},
    'amazon': {'url': 'https://comprehend.amazonaws.com', 'key': 'YOUR_KEY'}
}

audit = ContentModerationAudit(api_configs)

6 Ayyuka na Gaba

Binciken yana da muhimman tasiri ga tsarin tsarin kula da abubuwan ciki na gaba. Bincike na gaba ya kamata ya mayar da hankali kan haɓaka ƙirar da za ta iya rarrabe tsakanin kalaman ƙiyayya masu cutarwa da tattaunawar halaltacciyar asali. Kamar yadda aka lura a cikin takardar CycleGAN (Zhu et al., 2017), dabarun daidaita yanki na iya taimakawa magance sauye-sauyen rarraba a cikin ƙungiyoyin al'umma daban-daban. Bugu da ƙari, bin tsarin ƙungiyar Perspective API (Lees et al., 2022), tsarin na gaba ya kamata su haɗa da ƙa'idodin musamman na al'umma da sarrafa mahallin.

Hanyoyin da ke tasowa sun haɗa da:

Tsarin kula da abubuwan ciki masu yawa wanda ya haɗa rubutu, hoto, da binciken mahalli
Hanyoyin koyon tarayya don kiyaye sirri yayin inganta aikin ƙira
Dabarun AI masu bayyanawa don samar da yanke shawara na tsari masu bayyanawa
Daidaita tsarin tsari na al'adu daban-daban don dandamali na duniya

Binciken Asali: Takobi Mai Kaifi Biyu na Tsarin Kula da Abubuwan Ciki ta Atomatik

Wannan bincike yana ba da mahimman bayanai game da gaskiyar aiki na API na kasuwancin tsarin kula da abubuwan ciki, yana bayyana wani tsari mai tada hankali na son kai na tsari wanda ke shafar al'ummomi masu rauni ba daidai ba. Gano cewa API sau da yawa suna dogaro da sharuɗɗan asalin ƙungiya kamar "baƙar fata" don hasashen kalaman ƙiyayya yana jujjuya irin wannan batutuwan da aka gano a cikin sauran tsarin NLP, kamar son kai na launin fata da aka samo a cikin kayan aikin binciken ra'ayi na Sap et al. (2019). Abin da ya sa wannan binciken ya fi mahimmanci shi ne girmansa—bincika tambayoyi miliyan biyar a cikin bayanan bincike da yawa—da kuma cikakken tsarinsa wanda ya haɗa ma'aunin aiki tare da dabarun bayyananniyar.

Hanyar fasaha ta amfani da ƙimar SHAP da binciken rikici tana wakiltar ingantacciyar hanyar bincika tsarin akwatin baƙi. Wannan ya yi daidai da ƙarar da ake kira don bayyananniyar algorithm, kama da buƙatun a cikin sauran aikace-aikacen AI masu haɗari kamar binciken kiwon lafiya (Topol, 2019). Rage tsarin kalaman ƙiyayya na ɓoyayye da ake yi wa mutanen LGBTQIA+ ya fi damuwa musamman, saboda yana nuna cewa tsarin na yanzu sun kasa gane ingantattun nau'ikan wariyar da ba su dogara da zagage na zahiri ba.

Idan aka kwatanta da ƙirar buɗe ido da aka bincika a binciken da ya gabata (Röttger et al., 2021), API na kasuwanci suna nuna irin wannan tsarin son kai amma tare da yuwuwar tasiri mai girma a duniyar gas saboda yawaitar tura su. Shawarar don ingantacchen jagora kan saita kofa yana da mahimmanci musamman, saboda ingantacchen kofa yana wakiltar muhimmin wurin shiga tsakani don rage duka wuce gona da iri da ragewa. Aikin gaba ya kamata ya bincika kofofin daidaitawa waɗanda ke la'akari da mahalli da ƙa'idodin al'umma, kama da hanyoyin da aka tattauna a cikin littafin Adalci da Koyon Injina (Barocas et al., 2019).

Ƙayyadaddun binciken, gami da mayar da hankali ga abubuwan ciki na harshen Turanci da takamaiman ƙungiyoyin al'umma, suna nuna muhimman hanyoyi don bincike na gaba. Yayin da dandamali suka ƙara zama na duniya, haɓaka tsarin tsari waɗanda ke aiki a cikin harsuna da mahallin al'adu zai zama dole. Tsarin da aka kafa a cikin wannan takarda yana ba da tushe mai mahimmanci don irin waɗannan binciken al'adu.

7 Nassoshi

Hartmann, D., Oueslati, A., Staufer, D., Pohlmann, L., Munzert, S., & Heuer, H. (2025). Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variations. arXiv:2503.01623
Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A. (2019). The Risk of Racial Bias in Hate Speech Detection. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.
Röttger, P., Vidgen, B., Nguyen, D., Waseem, Z., Margetts, H., & Pierrehumbert, J. (2021). HateCheck: Functional Tests for Hate Speech Detection Models. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics.
Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. IEEE International Conference on Computer Vision.
Lees, A., Tran, V. Q., Tay, Y., Sorensen, J., Gupta, A., Metzler, D., & Vasserman, L. (2022). A New Generation of Perspective API: Efficient Multilingual Character-level Transformers. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning. fairmlbook.org.
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.