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By Dr.Sajjad Ali A suffering pervades the walled villages of Khyber Pakhtunkhwa, and the privacy of the homes in the city, in a language of suffering all distinct. The language of infertility is not the one used in medical textbooks but it is the one that speaks to life and death in Pashtun society. Being a childless woman is being defined by a lexicon of failure here, a failure that runs against the very ethos of Pashtunwali, where a person’s nasl (lineage) and naam (name) is everything. The condition is identical, but the language employed to use it as a weapon against women and men are very different, very vicious. It is a public indictment to her; a silent, emasculating damnation to him. The Woman’s Cross: The Public Indictment The worthiness of a Pashtun woman is arbitrarily squeezed into her reproductive capability. When she drops the one perceived duty, she is not infertile, she is cursed, useless and dangerous. The attack starts with an endless list of names and peghoor (tease). She is a banjh (barren), the most common and heart-breaking epithet. She is a shana (dry, unfruitful tree that bears no fruit). She is khaanch (empty), a hollow vessel with no substance. The elders of the family complain, “Mata plar na laree, khaanch de” (She hasn’t borne him a son, she is empty). Her body is compared to the barren land of Sheera zameena (salty, infertile land) which nothing can grow, or shara (fallow land). The childless woman or the one whose children have died is generally known as a kheeri, which translates as woman of ill omen, bearer of misfortune. She is slightly banned from being present at weddings or cradles (gharay). When a child is sick after the visit, people begin muttering, “Da kheeri da las na ye bimar sho” (After the touch of the kheeri the child fell sick). This spiritual banishment is so devastating! If her husband returns to marriage, or if the family goes bankrupt, she is considered a shamat (a bad omen, a curse to the family) and a manhoos (inauspicious, unlucky). The psychological take-home message is no less horrifying: she is not only failing to give birth; she is an active, evil agent of birthlessness and death and decay. She’s a curse for their existence. The peghoor is available in a dramatic fashion. A mother-in-law would see a hen with chicks and will loudly cry out, “Da charkha ham bachy kawal, khu da adamzad mung na shwe.” (Even this hen hatched chicks, but this human gave us nothing). Neighbours grumble, ‘Khudai da qahar da, cha ta kho pukha na we (She is God’s wrath, she hasn’t ripened for anybody—to mean that she has not become a mother). A cruel comic scene that is often acted out: The guest exclaims the house is a paradise, and the mother-in-law replies, “Kor kho jannat de, khu khaanch de, khoonda raghlay na de” [The house is a paradise, but it is empty, no fragrance has come into it]—singing about the fragrance of a child which reminds her of something she failed to do with her daughter-in-law. This puts her in an economic hostage position. She is a servile slave to make up for her “defect” because of the ever-present unspoken danger of a balka (co-wife). All the money is spent on her own “cure,” which includes dangerous dai (traditional birth attendant) concoctions, expensive visits to taweez-keeping (piir sahib) and futile taweez (amulet) usage. She will wear a tawiz laced with prayers for fertility and she will be made to drink water that has been washed with a dranzay (a special supplication)—her husband’s diagnosis will not even be suspected. This quest devours the family’s resources, everything aimed at the woman who is in a desperate struggle to remain not the family’s shamat. The Man’s Secret: The Silent Emasculation The man feels that the lack of producing babies is not something he has failed to do biologically, but something he has failed to do as a man. He can’t be brought into pity, because if he is, he’s acknowledging that he lost nang (honour). The words he has to say, the words he is not allowed to say, the words he can’t say at all: his torment is a poison that is surrounded by a wall of aggressive denial, but is intended to un-man him. If he is unable to generate a zo-ye (a son), then he is no longer a sar-ye (a man, a leader). His name is a na-mard, the worst of all names, that of a non-man. He is a susra—an utterly impotent, weakling man, a term of maximum contempt. A khassya (castrated bull, eunuch), who has no power and virility. He is a namardanz (an impotent creature). A popular bit of vicious poetry is: “Dwa kalah ye we-weted, khu goly ye na kholay shwe” (“He tried twice, but not a bud was able to blossom for him”, a poetic and mean way of saying that he was unable to start life. His true trial is in the hujra (man’s assembly). The taunt is never a literal arrow, but an arrow of some sort. A story will be told: “Da kala de, cha tofang walwela, khu dasy nese wa, che laande khassia we” (“There’s a man who wields a big rifle, and makes a lot of noise, but inside, he’s a eunuch”). They all laugh, the childless man is forced to laugh loudest, and the white-hot shame is his. The tale is best told in a popular social media comic strip that shows a doctor showing a semen analysis report to a furiously upset husband with a nervous wife looking on: Panel 1: A doctor shows a semen analysis report to a furious-looking husband, with his wife sitting nervously. The Doctor says, “The report reads: azoospermia.The Doctor says, “The report reads azoospermia. Panel 2: A huge, broken speech bubble bursts out of the husband’s mouth: “Tor! Tor! Sta da peghalay!” (“Lies! “It’s your slander!”)

By Syeda Samar Zahra Zaidi When you ask an AI chatbot a question it does not know the answer to, an odd thing can happen: instead of hesitating or saying, “I am not sure!” it confidently delivers a tidy, well-structured, completely fabricated response—often with fake citations, fictional figures, and an authoritative tone. This phenomenon is known as the ‘hallucination trap ’, and it may be the most significant barrier between trust and artificial intelligence. I was introduced to the term “AI hallucination” for the first time in the workshop under the project titled “Digital Trust and Free Expression in the AI Era” at Department of Media and Development Communication, University of the Punjab, supported by the U.S. Mission to Pakistan in partnership with the Pakistan-U.S. Alumni Network (PUAN). I learned that a “hallucination” occurs when a language model produces information that sounds convincing but is factually incorrect or completely made up. Examples include inventing a legal case that never existed, citing a research paper that doesn’t exist or boldly misrepresenting a historical date. What is disturbing is not merely that AI can be wrong-humans make mistake too- but rather the way it presents that incorrect information with clearly, confidence and without a trace of doubt. Large language models are essentially highly complex pattern predictors trained to produce the most likely next word in a sequence. As a result, when a model has encountered a topic thousands of times in its training data, the predictors are typically accurate. However, when it comes across a gap, an unidentified fact or a question at the outer edge of its knowledge, it doesn’t shift into a hesitant mode. Instead, it continues doing exactly what it was trained to do: generate a logical, fluent piece of writing, producing a response that is structurally sound and grammatically correct but based on nothing. In human communication, confidence is generally perceived as a sign of strength which is important. We naturally prefer to trust a calm, reliable voice over a hesitant one. AI systems unintentionally exploit the tendency because their language remains confident whether they are right or wrong. The risks increase with the use of AI, as students may unintentionally include false information into their work and journalists have published articles citing artificially created sources and quotes, negatively affecting the credibility of their publications. Such inaccuracies influence what millions of people see, believe or share, thereby amplifying fabricated content. The reason AI does not simply say “I don’t know” is partly due to the way these systems are trained. Many models are designed to provide helpful, comprehensive-sounding answers and are rarely rewarded for expressing doubt during training. This means that a model that frequently acknowledges uncertainty may be judged as less helpful than one that consistently provides an answer, even when that confidence is occasionally misplaced. Such incorrectly aligned incentives arise when the training process gradually teaches the model that sounding confident is more valuable than being accuracy. Retrieval-augmented generation, which relies on responses in actual, verifiable records rather than internal memory alone, improved training techniques that explicitly account for uncertainty; and systems built to simply cite sources. So, users can independently verify claims are just a few of the ways developers are using to addresses this problem. While none of these solutions fully eliminate hallucination yet, taken together they represent a major shift from blind confidence toward earned trust. The most dangerous outcome is not an AI that makes mistakes but a culture of users who stop questioning because the system always sounds so sure of itself.   Therefore, until AI becomes more reliably aware of it own limitation, part of the responsibility falls on the people who use it: treating AI-generated output as a starting point rather than an endpoint, cross checking important facts against credible sources. In the end, the hallucination trap serves as a reminder that illusion is not the same as truth. As AI becomes increasingly common in education, digital media and everyday life, understanding why it occasionally lies with confidence is not only a technical curiosity but also a fundamental aspect of digital literacy. We must strengthen our ability to verify the information provided by AI as they become more persuasive. The views, thoughts, and opinions expressed in this blog are solely those of the author and do not necessarily reflect the official policy or position of the U.S. Mission to Pakistan or USEFP. The writer, Syeda Samar Zahra Zaidi, is a student of Media and Development Communication at  University of the Punjab, Lahore and can be reached at samarzahra456@gmail.com.